Image processing Books

Book SynopsisVisible and Clear: Finding Tiny Objects in Difference Map.- Rethinking Image Super Resolution from Training Data Perspectives.- BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering.- Efficient Inference of Vision Instruction-Following Models with Elastic Cache.- FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior.- Learning to Robustly Reconstruct Dynamic Scenes from Low-light Spike Streams.- MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object Detection.- WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models.- Interactive 3D Object Detection with Prompts.- How Video Meetings Change Your Expression.- GRACE: Graph-Based Contextual Debiasing for Fair Visual Question Answering.- Neural Volumetric World Models for Autonomous Driving.- IVTP: Instruction-guided Visual Token Pruning for Large Vision-Language Models.- RegionDrag: Fast Region-Based Image Editing with Diffusion Models.- On the Error Analysis of 3D Gaussian Splatting and an Optimal Projection Strategy.- Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding.- Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration.- GRA: Detecting Oriented Objects through Group-wise Rotating and Attention.- A Simple Knowledge Distillation Framework for Generalizable Vision-Language Models.- Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer.- CSOT: Cross-Scan Object Transfer for Semi-Supervised LiDAR Object Detection.- Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation.- ShareGPT4V: Improving Large Multi-Modal Models with Better Captions.- Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation.- Invertible Neural Warp for NeRF.- Enhancing Vectorized Map Perception with Historical Rasterized Maps.- Efficient and Versatile Robust Fine-Tuning of Zero-shot Models.

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Book SynopsisVideoMamba: Spatio-Temporal Selective State Space Model.- Text to Layer-wise 3D Clothed Human Generation.- Texture-GS: Disentangle the Geometry and Texture for 3D Gaussian Splatting Editing.- Fully Sparse 3D Occupancy Prediction.- Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data.- CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field.- Shifted Autoencoders for Point Annotation Restoration in Object Counting.- PointLLM: Empowering Large Language Models to Understand Point Clouds.- GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections.- Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving.- Enhancing Diffusion Models with Text-Encoder Reinforcement Learning.- Asymmetric Mask Scheme for Self-Supervised Real Image Denoising.- Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation.- BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting.- Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesis.- BaSIC: BayesNet Structure Learning for Computational Scalable Neural Image Compression.- FlexAttention for Efficient High-Resolution Vision-Language Models.- Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable Repainting.- AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation.- Spatially-Variant Degradation Model for Dataset-free Super-resolution.- DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation.- Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence.- Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation.- EAFormer: Scene Text Segmentation with Edge-Aware Transformers.- Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects.- DetailSemNet: Elevating Signature Verification through Detail-Semantic Integration.- LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation.

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Book SynopsisInstructIR: High-Quality Image Restoration Following Human Instructions.- Asynchronous Large Language Model Enhanced Planner for Autonomous Driving.- Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation.- LayoutFlow: Flow Matching for Layout Generation.- Making Large Language Models Better Planners with Reasoning-Decision Alignment.- R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection.- Representation Enhancement-Stabilization: Reducing Bias-Variance of Domain Generalization.- Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference.- An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes.- STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians.- RGBD GS-ICP SLAM.- Efficient NeRF Optimization - Not All Samples Remain Equally Hard.- Revisiting Calibration of Wide-Angle Radially Symmetric Cameras.- Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs.-

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Book SynopsisEgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval.- Synchronization of Projective Transformations.- TLControl: Trajectory and Language Control for Human Motion Synthesis.- Insect Identification in the Wild: The AMI Dataset.- Cross-view image geo-localization with Panorama-BEV Co-Retrieval Network.- F-HOI: Toward Fine-grained Semantic-Aligned 3D Human-Object Interactions.- Test-time Model Adaptation for Image Reconstruction Using Self-supervised Adaptive Layers.- SHIC: Shape-Image Correspondences with no Keypoint Supervision.- GenRC: Generative 3D Room Completion from Sparse Image Collections.- A Probability-guided Sampler for Neural Implicit Surface Rendering.- ReMatching: Low-Resolution Representations for Scalable Shape Correspondence.- Where am I? Scene Retrieval with Language.- This Probably Looks Exactly Like That: An Invertible Prototypical Network.- Arc2Face: A Foundation Model for ID-Consistent Human Faces.- PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations.- Revisiting Feature Disentanglement Strategy in Diffusion Training and Breaking Conditional Independence Assumption in Sampling.- SweepNet: Unsupervised Learning Shape Abstraction via Neural Sweepers.- Leveraging Thermal Modality to Enhance Reconstruction in Low-Light Conditions.- On the Viability of Monocular Depth Pre-training for Semantic Segmentation.- Fairness-aware Vision Transformer via Debiased Self-Attention.- EgoPet: Egomotion and Interaction Data from an Animal's Perspective.- Deep Companion Learning: Enhancing Generalization Through Historical Consistency.- Neural graphics texture compression supporting random access.- Contrastive Learning with Synthetic Positives.- GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features.- Interpretability-Guided Test-Time Adversarial Defense.- DIM: Dyadic Interaction Modeling for Social Behavior Generation.

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Book SynopsisAudio-Synchronized Visual Animation.- Expressive Whole-Body 3D Gaussian Avatar.- Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning.- Controllable Human-Object Interaction Synthesis.- High-Fidelity and Transferable NeRF Editing by Frequency Decomposition.- DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects.- PAV: Personalized Head Avatar from Unstructured Video Collection.- Strike a Balance in Continual Panoptic Segmentation.- In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation.- MultiDelete for Multimodal Machine Unlearning.- Unified Local-Cloud Decision-Making via Reinforcement Learning.- UniTalker: Scaling up Audio-Driven 3D Facial Animation through A Unified Model.- Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation.- Efficient Frequency-Domain Image Deraining with Contrastive Regularization.- Stitched ViTs are Flexible Vision Backbones.- TrajPrompt: Aligning Color Trajectory with Vision-Language Representations.- SemReg: Semantics Constrained Point Cloud Registration.- Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views.- RoScenes: A Large-scale Multi-view 3D Dataset for Roadside Perception.- ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer.- Language-Driven Physics-Based Scene Synthesis and Editing via Feature Splatting.- AlignDiff: Aligning Diffusion Models for General Few-Shot Segmentation.- SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition.- R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding.- Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors.- Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering.- DomainFusion: Generalizing To Unseen Domains with Latent Diffusion Models.

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Book SynopsisThe multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29?October 4, 2024.The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.

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Book SynopsisKFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter.- Physical-Based Event Camera Simulator.- V-IRL: Grounding Virtual Intelligence in Real Life.- Adversarial Prompt Tuning for Vision-Language Models.- Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing.- Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation.- CC-SAM: Enhancing SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation.- An Efficient and Effective Transformer Decoder-Based Framework  for Multi-Task Visual Grounding.- Think2Drive: Efficient Reinforcement Learning by Thinking with Latent World Model for Autonomous Driving (in CARLA-v2).- PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion.- X-InstructBLIP: A Framework for Aligning Image, 3D, Audio, Video to LLMs and its Emergent Cross-modal Reasoning.- Learning Neural Volumetric Pose Features for Camera Localization.- Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation.- REFRAME: Reflective Surface Real-Time Rendering for Mobile Devices.- Self-Training Room Layout via Geometry-aware Ray-casting.- Closed-Loop Unsupervised Representation Disentanglement with $\beta$-VAE Distillation and Diffusion Probabilistic Feedback.- Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective.- Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization.- ZoLA: Zero-Shot Creative Long Animation Generation with Short Video Model.- Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach.- Restore Anything with Masks:  Leveraging Mask Image Modeling for Blind All-in-One Image Restoration.- When Fast Fourier Transform Meets Transformer for Image Restoration.- Dolphins: Multimodal Language Model for Driving.- Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model.- CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection.- Placing Objects in Context via Inpainting for Out-of-distribution Segmentation.- Textual Grounding for Open-vocabulary Visual Information Extraction in Layout-diversified Documents.

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Book SynopsisThe multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29?October 4, 2024.The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.

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Book SynopsisRobust Nearest Neighbors for Source-Free Domain Adaptation under Class Distribution Shift.- Chains of Diffusion Models.- Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models.- Feature Diversification and Adaptation for Federated Domain Generalization.- Grounding Image Matching in 3D with MASt3R.- TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Sampling.- RoDUS: Robust Decomposition of Static and Dynamic Elements in Urban Scenes.- RecurrentBEV: A Long-term Temporal Fusion Framework for Multi-view 3D Detection.- Efficient Bias Mitigation Without Privileged Information.- MC-PanDA: Mask Confidence for Panoptic Domain Adaptation.- Learning Neural Deformation Representation for 4D Dynamic Shape Generation.- Dynamic Guidance Adversarial Distillation with Enhanced Teacher Knowledge.- Decomposition Betters Tracking Everything Everywhere.- Straightforward Layer-wise Pruning for More Efficient Visual Adaptation.- Synchronization is

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Book SynopsisSeA: Semantic Adversarial Augmentation for Last Layer Features from Unsupervised Representation Learning.- Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents.- Rethinking Fast Adversarial Training: A Splitting Technique To Overcome Catastrophic Overfitting.- Quality Assured: Rethinking Annotation Strategies in Imaging AI.- BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues.- Enhancing Plausibility Evaluation for Generated Designs with Denoising Autoencoder.- Weakly-Supervised 3D Hand Reconstruction with Knowledge Prior and Uncertainty Guidance.- 3D Reconstruction of Objects in Hands without Real World 3D Supervision.- To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning.- Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer.- 3D Hand Pose Estimation in Everyday Egocentric Images.- Mitigating Perspective Distort

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Book SynopsisWeak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection.- Domesticating SAM for Breast Ultrasound Image Segmentation via Spatial-frequency Fusion and Uncertainty Correction.- CanonicalFusion: Generating Drivable 3D Human Avatars from Multiple Images.- Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation.- Uni3DL: A Unified Model for 3D Vision-Language Understanding.- Object-Aware NIR-to-Visible Translation.- PaPr: Training-Free One-Step Patch Pruning with Lightweight ConvNets for Faster Inference.- GENIXER: Empowering Multimodal Large Language Models as a Powerful Data Generator.- BLINK: Multimodal Large Language Models Can See but Not Perceive.- AFF-ttention! Affordances and Attention models for Short-Term Object Interaction Anticipation.- PreLAR: World Model Pre-training  with Learnable Action Representation.- Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot.- De-co

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Book SynopsisThe multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.

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Book SynopsisOvSW: Overcoming Silent Weights for Accurate Binary Neural Networks.- Multistain Pretraining for Slide Representation Learning in Pathology.- T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy.- Harmonizing knowledge Transfer in Neural Network with Unified Distillation.- Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data.- Click Prompt Learning with Optimal Transport for Interactive Segmentation.- 3D Human Pose Estimation via Non-Causal Retentive Networks.- OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection.- 6DoF Head Pose Estimation through Explicit Bidirectional Interaction with Face Geometry.- Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging.- Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition.- Enhancing Tampered Text Detection through Frequency Feature Fusion and Decomposition.- Modeling Label Correlations with Latent Context for Multi-Label Recognition.- L

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Book SynopsisAWOL: Analysis WithOut synthesis using Language.- OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework.- M3DBench: Towards Omni 3D Assistant with Interleaved Multi-modal Instructions.- MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes.- End-to-End Rate-Distortion Optimized 3D Gaussian Representation.- Temporal Residual Jacobians for Rig-free Motion Transfer.- LetsMap: Unsupervised Representation Learning for Label-Efficient Semantic BEV Mapping.- Deblurring 3D Gaussian Splatting.- Taming Lookup Tables for Efficient Image Retouching.- DualDn: Dual-domain Denoising via Differentiable ISP.- Quantization-Friendly Winograd Transformations for Convolutional Neural Networks.- A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting.- Self-supervised Shape Completion via Involution and Implicit Correspondences.- From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlatio

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Book SynopsisThe multi-volume set of LNCS books with volume numbers 15059 upto 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29?October 4, 2024.The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as Computer vision, Machine learning, Deep neural networks, Reinforcement learning, Object recognition, Image classification, Image processing, Object detection, Semantic segmentation, Human pose estimation, 3D reconstruction, Stereo vision, Computational photography, Neural networks, Image coding, Image reconstruction and Motion estimation.

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Book SynopsisSegmentation: Generating Synthetic Tree Point Clouds for Deep Learning Applications in Remote Sensing.- Image Segmentation by Latent Space Phase-Gating with Applications in High-Content Screening.- Evaluating Segmentation of Human Body Parts across Datasets. Recognition: Unsupervised Effectiveness Estimation Measure Based on Rank Correlation for Image Retrieval.- VLPSR: Enhancing Zero-shot Object ReID with Vision-language Model.- Sign Language Recognition using Visual Hand Landmarks and the Parameters of American Sign Language. ST: Generalization in Visual Machine Learning: Self-Supervised Segmentation to Pose Estimation Model for Mechanical Systems with Complex Kinematics.- Selective Noise-Aided Machine Unlearning with Deep Feature Visualization.- Investigating the Impact of a Foundational Medical Image Model for CT Classification. ST: Vision and Robotics for Agriculture: Vision-based Xylem Wetness Classification in Stem Water Potential Determination.- HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning.- Machine Vision and Deep Learning, for Robotic Harvesting of Shiitake Mushrooms.- Video analyses of Water Drop Penetration Time Using Temporal Action Localization for Evaluating Soil Water Repellency.- SAMPLS: A prompt engineering approach using Segment-Anything-Model for PLant Science research. Virtual Reality: Impact of Relevant Augmented Reality Information on Human Performance.- Toward Dynamic NPC Interactions: Integrating GPT-Driven Agents in 3D Virtual Environments.- Exploring Ecological Validity: A Comparative Study of the Mere Exposure Effect on Screens and in Immersive Virtual Reality.- Increasing Training Effciency of Motion-Intensive Virtual Reality Training with Adaptations based on Physiological Measurement Data. Applications: Leveraging Zero-Shot Learning on Street-View Imagery for Built Environment Variable Analysis.- Enhancing Classification of Aquatic Species through Supervised Contrastive Learning and Advanced Image Super-Resolution.- Automated Corrosion Identification in Metal Imagery: Traditional vs. Deep Learning.- Underwater Image Restoration using Light Attenuation.- HCC: An explainable framework for classifying discomfort from video. Poster: Enhanced Maritime Safety through Deep Learning and Feature Selection.- Discrete Anomalous Regions (DAR) - going beyond heatmaps and predicting actionable discrete regions.- Learning Flight Path Based on Recording Image and Flight Operation.- MobileNetV2-Enhanced Depth Map Super-Resolution through Multi-Scale Image Guidance.- Road Surface Material Recognition from Dashboard Cameras.- Embedded-ViT: A Framework for Embedded Deployment of Vision-Transformer in Medical Applications.- An Image-Based Method for Defect Detection on Metal Surfaces.- Real-Time Evaluation of Aircraft Instruments.- Enhancing Learned Image Compression via Cross Window-based Attention.- A Design of Real-Time Style-Transfer Operations in a Game Engine.- PLOV: A Visualization Tool For Exploring Visibility in Family Living Situations.- Exploring Gesture-Based Interaction in Smartwatch Games: A Comparative Study between Continuous Gesture Recognition and Hidden Markov Models.

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Book SynopsisThis book constitutes the refereed proceedings of the 21st International Conference on Virtual Reality and Mixed Reality, EuroXR 2024, held in Athens, Greece, during November 27?29, 2024.The 14 full papers presented together with 1 short paper were carefully reviewed and selected from 47 submissions.The papers are grouped into the following topics: Designing Experiences,Human Factors,Rendering and Visualization,Interaction Techniques, andEducation and Training.EuroXR aims to foster engagement between European industries, academia, and the public sector, to promote the development and deployment of XR tech niques in new and emerging, but also in existing fields.

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Book SynopsisDARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector LoRA of the Foundation Model.- LocalMamba: Visual State Space Model with Windowed Selective Scan.- Compositional Text-to-Image Generation with Feedforward Layout Generation.- PackMamba:Efficient Processing of Variable-Length Sequences in Mamba training.- Down-Sampling Inter-Layer Adapter for Parameter and Computation Efficient Ultra-Fine-Grained Image Recognition.- Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy.- LLaMA-NAS: Efficient Neural Architecture Search for Large Language Models.- Improving Hyperparameter Optimization with Checkpointed Model Weights.- MagicDec: Breaking the Latency-Throughput Tradeoff for Long Contexts with Speculative Decoding.- Mixed Non-linear Quantization for Vision Transformers.- CycleBNN: Cyclic Precision Training in Binary Neural Networks.- DailyMAE: Towards Pretraining Masked Autoencoders in One Day.- EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization.- Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes.- LightAvatar: Efficient Head Avatar as Dynamic Neural Light Field.- Giving each task what it needs - leveraging structured sparsity for tailored multi-task learning.- ERF-NAS: Efficient Receptive Field-based Zero-Shot NAS for Object Detection.- CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge.- Memory-Optimized Once-For-All network.- Famba-V: Fast Vision Mamba with Cross-Layer Token Fusion.- Latent Distillation for Continual Object Detection at the Edge.- MCUBench: A Benchmark of Tiny Object Detectors on MCUs.- Optimizing Resource Consumption in Diffusion Models through Hallucination Early Detection.

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Book SynopsisPractical Dataset Distillation Based on Deep Support Vectors.- Leveraging FINCH and K-means for Enhanced Cluster-Based Instance Selection.- GSTAM: Efficient Graph Distillation with Structural Attention-Matching.- DiM: Distilling Dataset into Generative Model.- Generative Dataset Distillation using Min-Max Diffusion Model.- Data-Efficient Generation for Dataset Distillation.- Generative Dataset Distillation Based on Diffusion Model.- Optimizing Dataset Distillation Using DATM: Adjusting Learning Rate and Upper Bound.- Well Begun is Half Done: The Importance of Initialization in Dataset Distillation.- Enhancing Dataset Distillation via Label Inconsistency Elimination and Learning Pattern Refinement.- A Spitting Image: Modular Superpixel Tokenization in Vision Transformers.- NIGHT - Non-Line-of-Sight Imaging from indirect Time of Flight data.- Self-accumulative Vision Transformer for Bone Age Assessment using the Sauvegrain Method.- FastTalker: Jointly Generating Speech and Conversational Gestures from Text.- Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification.- CMMD: Contrastive Multi-Modal Diffusion for Video-Audio Conditional Modeling.- Unveiling Visual Biases in Audio-Visual Localization Benchmarks.- AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition.- Towards Multimodal In-Context Learning for Vision & Language Models.

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Book SynopsisFetal Brain MRI: Reconstruction, Segmentation, and Development.- Automatic Quality Control in Multi-Centric Fetal Brain MRI Super-Resolution Reconstruction.- D SVG: Diffusion-Based Slice-to-Volume Generation with Implicit Neural Representation for Fetal Brain MRI.- Continuous Spatio Temporal Representation with Implicit Neural Networks for Fetal Brain MRI Atlas Construction.- Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation.- Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology Informed Domain Randomization.- Physics Informed Joint Multi TE Super Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping.- Robustness and Diagnostic Performance of Super Resolution Fetal Brain MRI.- Enhancing Fetal Brain MRI Segmentation in Ventriculomegaly Using Generative AI Augmented Pathological Data.- FetGEs a Deep Learning Approach for Fetal MRI Ganglionic Eminence Segmentation.- Quantifying Fetal Periventricular White Matter Development Using Multimodal MRI.- LLM and Functional Imaging in Fetal and Placental MRI.- FetalExtract LLM: Structured Information Extraction from Free Text Fetal MRI Reports Based on Privacy Ensuring Open weights Large Language Models.- Automated Biometry for Assessing Cephalopelvic Disproportion in 3D 0.55T Fetal MRI at Term.- Contrast Invariant Self Supervised Segmentation for Quantitative Placental MRI.- PUUMA Functional MRI Prediction of Gestational Age at Birth and Preterm Risk.- Robust Alignment of the Human Embryo in 3D Ultrasound Using PCA and Classifier Ensembles.- Neonatal and Pediatric Imaging.- NEUBORN: The Neurodevelopmental Evolution Framework Using Biomechanical Remodeling.- Anatomically Informed Dynamic Weighting for Semi Supervised Fetal MRI Segmentation.- Towards a Comprehensive Morphological, Dynamic, and Functional MRI Investigation of the Pediatric Bowel at 0.55T.

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Book Synopsis.- 3D Reconstruction and Imaging..- DualTrack: Sensorless 3D Ultrasound needs Local and Global Context..- Modulated INR with Prior Embeddings for Ultrasound Imaging Reconstruction..- DiffUS: Differentiable Ultrasound Rendering from Volumetric Imaging..- 3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices..- 3D Ultrasound Volume Reconstruction using a CNN-Transformer model and IMU data..- Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction..- Registration, Representation and Generation..- Robust rigid MRI-TRUS registration using attention-CNN and ICP..- Det-SAMReg: Few-Shot Medical Image Registration using Vision Foundation Models..- D.A.R.K.: Dynamic Graphs based Angle-aware Registration of Knee Ultrasound Point Clouds..- The Impact of Biomechanical Quantities on PINNs-based Medical Image Registration..- VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos..- From Transthoracic to Transesophageal: Cross-Modality Generation using LoRA Diffusion..- DiFUSAL: Diffusion-Based Fetal Ultrasound Synthesis with Active Learning..- Image acquisition, segmentation and interpretability..- Motion-enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module..- UGFNet: Uncertainty-Guided Graph Neural Network with Frequency-Aware Feature Fusion for Breast Ultrasound Segmentation..- L-FUSION: Laplacian Fetal Ultrasound Segmentation & Uncertainty Estimation..- Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment..- Guide2Heart: Proximity Guidance for Standard Echocardiographic View..- Classification and Measurements. .- HiProtoNet: Hyperbolic Hierarchy-aware Part Prototypes for Aortic Stenosis Severity Classification..- COVID-19 Severity Prediction from Lung Ultrasound via Dynamic Gated Multi-Instance Learning..- WiseLVAM: A Novel Framework For Left Ventrical Automatic Measurements..- Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification..- TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction..- Anatomically Constrained Transformers for Cardiac Amyloidosis Classification.

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Book Synopsis.- Risk management, Uncertainty interpretation and visualisation.- MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos..- Unsupervised Artifact Detection and Quantification via Contrastive Learning with Noise Reference..-Disagreement-Driven Uncertainty Quantification in Late GadoliniumEnhancement Cardiac MRI..- Is Uncertainty Quantification a Viable Alternative to Learned Deferral?..- Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation..- Numerical Uncertainty in Linear Registration: An Experimental Study..- Domain shift and out-of-distribution management.- SPARTA: Spectral Prompt Agnostic Adversarial Attack on Medical Vision-Language Models..- Label-free estimation of clinically relevant performance metrics under distribution shifts..- Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories..- SCORPION: Addressing Scanner-Induced Variability in Histopathology..- LEXU: Learning from Expert Disagreement for Single-Pass Uncertainty Estimation in Medical Image Segmentation..- Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift..- Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching..- Uncertainty Calibration.- Multi-Rater Calibration Error Estimation..- Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics..- Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction..- Evaluation of Monte Carlo Dropout for Uncertainty Quantification in Multi-task Deep Learning-Based Glioma Subtyping..- Uncertainty modelling and estimation, Bayesian deep learning.- Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification..- Uncertainty-Aware Classification: A Human-Guided Bayesian Deep Learning Framework..- Empirical Bayesian Methods and BNNs for Medical OOD Detection..- A Proper Structured Prior for Bayesian T1 Mapping..- Bayesian MRI Reconstruction with Structured Uncertainty Distributions.

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Book Synopsis.- A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration..- A Data-Driven Approach to Optimise Parameters of a Computational Digital Twin Model in Response to SBRT on MR-Linac..- FMIC-AI: Annotation-Free Tumor Cell Detection in Fluorescence Microscopy via Self-Supervised Anomaly Detection..- Score-based Diffusion Model for Unpaired Virtual Histology Staining..- Redefining Spectral Unmixing for In-Vivo BrainTissue Analysis from Hyperspectral Imaging..- CT Image Segmentation Using Frequency Domain Feature-Assisted Selective Long Memory State Space Model..- Towards Robust Skin Lesion Classification: Lesion Segmentation, Mole Collision Simulation and Hierarchical learning..- Key Clinical Parameters Detection and Ovarian Tumor Benign/Malignant Classification in Multi-Modal Ultrasound Images via a Multi-Task Model..- OG-SAM: Enhancing Multi-Organ Segmentation with Organogenesis-Based Adaptive Modeling..- CoMoSeg: Anatomical Consistency and Cross Modality Guidance for Robust Brain Tumor Segmentation Using Partially Labeled MR Sequences..- Region-aware Diagnosis of Clinically Significant Prostate Cancer via Semi-supervised Learning Segmentation..- GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis..- Dual-Guided 3D Liver CT Image Generation for Medical Analysis..- HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation..- Projection-Driven Robust Motion Compensation for CBCT Using a Patient-Specific Model Learned from Prior Scans..- Revealing New Possibilities for Breast MRI Enhancement: Mamba-Driven Cross-Attention GAN with VMKANet..- Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma.

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Book SynopsisDer „Klassiker" der Bildverarbeitung liefert eine fundierte und anwendungsgerechte Einführung in die wichtigsten Methoden und in ausgewählte Verfahren. Seine besondere Stärke: große Detailgenauigkeit, präzise algorithmische Beschreibung sowie die unmittelbare Verbindung zwischen mathematischer Beschreibung und konkreter Implementierung. Übungsaufgaben und Code-Beispiele runden die Darstellungen ab. Source Code und ergänzende Materialien finden sich auf der Internetseite www.imagingbook.com. Die Neuauflage wurde überarbeitet und erweitert.Table of ContentsCrunching Pixels.- Digitale Bilder.- ImageJ.- Histogramme.- Punktoperationen.- Filter.- Kanten und Konturen.- Auffinden von Eckpunkten.- Detektion einfacher Kurven.- Morphologische Filter.- Regionen in Binärbildern.- Farbbilder.- Einführung in Spektraltechniken.- Die diskrete Fouriertransformation in 2D.- Die diskrete Kosinustransformation (DCT).- Geometrische Bildoperationen.- Bildvergleich.- Automatische Schwellwertoperationen.- Filter für Farbbilder.- Lokale, invariante Bildmerkmale.- Erzeugung von Bildrauschen..- Quantitative Qualitätsmaße für Bilder.- Anhang A: Mathematische Grundlagen.- Anhang B: Java-Notizen.- Literaturverzeichnis.- Sachverzeichnis.

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Book SynopsisThis is the second edition of a very popular book on DICOM that introduces this complex standard from a very practical point of view. It is aimed at a broad audience of radiologists, clinical administrators, information technologists, medical students, and lecturers. The book provides a gradual, down to earth introduction to DICOM, accompanied by an analysis of the most common problems associated with its implementation. Compared with the first edition, many improvements and additions have been made, based on feedback from readers. Whether you are running a teleradiology project or writing DICOM software, this book will provide you with clear and helpful guidance. It will prepare you for any DICOM projects or problem solving, and assist you in taking full advantage of multifaceted DICOM functionality.Trade ReviewFrom the reviews: "This book covers the structure and application of the DICOM standard in enough detail to be helpful to the general user of medical images, as well as to a programmer who is developing an image management system. … the text can also serve as a reference for those getting more involved with applications that use DICOM or those who want to develop their own DICOM application. … I would recommend this book to anyone interested in the DICOM standard." (Donald Peck, The Journal of Nuclear Medicine, Vol. 50 (8), August, 2009) "There have been many books trying to explain the wonders of Dicom … . This book goes to a lot of detail … . descriptions contained provide a useful resource when trying to integrate systems. … The charts and graphics are easy to use and explain the mysteries well. … I would recommend this book as a good practical introduction and survival guide … . I would recommend this book, along with a practical Dicom course … for those starting out in this area." (Chris Bull, RAD Magazine, July, 2009)Table of ContentsIntroduction to DICOM: What is DICOM?- How does DICOM work?- Where do you get DICOM from?- A Brief History of DICOM.- DICOM and Clinical Data: Parlez-vous DICOM?- Medical Images in DICOM.- DICOM Communications: DICOM SOPs: Basic.- DICOM SOPs: Beyond Basic.- DICOM Associations.- DICOM Media and Security: DICOM Media: Files, Folders, and DICOMDIRs.- DICOM Security.- Incompatibility of Compatible.- Advanced Topics: DICOM and Teleradiology.- Standards and System Integration in Digital Medicine.- Disaster PACS Planning and Management.- DICOM Software Development.- DICOM Implementation Plans.- DICOM FAQs.- Appendix.

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Book SynopsisIn the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles. Table of ContentsIntroduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.

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Book SynopsisThis book is a collection of the papers accepted by the ICIVIS 2021—The International Conference on Image, Vision and Intelligent Systems held on June 15–17, 2021, in Changsha, China. The topics focus but are not limited to image, vision and intelligent systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings.Table of ContentsImage and Vision.- Intelligent Systems.- Computation and Application.

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Book SynopsisThis book focuses on end-to-end robotic applications using vision and control algorithms, exposing its readers to design innovative solutions towards sensors-guided robotic bin-picking and assembly in an unstructured environment. The use of sensor fusion is demonstrated through a bin-picking task of texture-less cylindrical objects. The system identification techniques are also discussed for obtaining precise kinematic and dynamic parameters of an industrial robot which facilitates the control schemes to perform pick-and-place tasks autonomously without any interference from the user. The uniqueness of this book lies in a judicious balance between theory and technology within the context of industrial application. Therefore, it will be valuable to researchers working in the area of vision- and force control- based robotics, as well as beginners in this inter-disciplinary area, as it deals with the basics and technologically advanced research strategies.Table of ContentsIntroduction.- Vision System and Calibration.- Uncertainty and Sensitivity Analysis.- Identification.- Force Control and Assembly.- Integrated Assembly and Performance Evaluation.- Conclusion.- Vision and Uncertainty Analysis.- Robot Jacobian.- Code Snippets and Experimental Videos.

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Book SynopsisThis book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field.Table of ContentsWhat's In This Book and Why?.- Media Forensics in the Age of Disinformation.- Computational Imaging.- Sensor Fingerprints: Camera Identification and Beyond.- Source Camera Attribution from Videos.- Source Camera Model Identification.- GAN Fingerprints in Face Image Synthesis.- Physical Integrity.

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Book SynopsisThis book consists of a collection of the high-quality research articles in the field of computer vision and robotics which are presented in the International Conference on Computer Vision and Robotics (CVR 2021), organized by BBD University Lucknow, India, during 7–8 August 2021. The book discusses applications of computer vision and robotics in the fields like medical science, defence, and smart city planning. The book presents recent works from researchers, academicians, industry, and policy makers.Table of ContentsAI Techniques for Swedish Leaf Classification.- Designing a Recommender System for Articles using Implicit Feedback.- Personality Prediction based on Twitter Tweets.- Music Therapy for Mood Transformation based on Deep Learning Framework.- Rice Crop Diseases and Pest Detection using Edge Detec-tion Techniques and Convolution Neural Network.- Spatial Analyses of Cyclone Amphan Induced Flood Inundation Mapping Using Sentinel-1A SAR Images Through GEE Cloud.- Classification through Data Mining Algorithm.- Early Detection of Diabetic Retinopathy using Multimodal Approach.- Intelligent Techniques for Crowd Detection And People Counting- A Systematic_Study.- Twitter Sentiment Analysis of Public Opinion on COVID-19 Vaccines.

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Book SynopsisThe volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.Trade Review“Each application is described with sufficient detail to give the reader an understanding of how AI is used and how its use compares with older tools used for the same purposes. The writing is clear … and an excessive use of acronyms. Relevant images and tables enhance the reader’s understanding; many references accompany each chapter. This book should appeal to those interested in either AI or the field of solar astronomy.” (G. R. Mayforth, Computing Reviews, November 22, 2023)Table of ContentsChapter 1: Introduction Chapter 2: Classical deep learning models Chapter 3: Deep learning in solar image classification tasks Chapter 4: Deep learning in solar object detection tasks · Active Region (AR) detection · EUV waves detection Chapter 5: Deep learning in solar image generation tasks · Deconvolution of aperture synthesis · Recovering over-exposed solar image · Generating magnetogram from EUV image · Generating magnetogram from H-alpha Chapter 6: Deep learning in solar forecasting tasks · Flare forecast · F10.7c forecast

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Book SynopsisThis book constitutes the refereed proceedings of the Third International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021. The papers are organized according to the following topical sections: data science and big data; image processing and computer vision; machine learning and computational intelligence; network and cybersecurity. This book aims to develop an understanding of image processing, networks, and data modeling by using various machine learning algorithms for a wide range of real-world applications. In addition to providing basic principles of data processing, this book teaches standard models and algorithms for data and image analysis. Table of ContentsA Methodological review of Time Series Forecasting with Deep Learning Model : A Case study on Electricity Load and Price Prediction.- A Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print.- Prostate Cancer Grading using Multistage DeepNeural Networks.

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Book SynopsisThis book is a collection of the high-quality research articles in the field of computer vision and robotics which are presented in International Conference on Computer Vision and Robotics (ICCVR 2022), organized by BBD University Lucknow India, during 21 – 22 May 2022. The book discusses applications of computer vision and robotics in the fields like medical science, defence and smart city planning. This book presents recent works from researchers, academicians, industry, and policy makers.Table of ContentsStory Telling: Learning to Visualize Sentences Through Generated Scenes.- A Novel Processing of Scalable Web Log Data using Map Reduce Framework.- A Review of Disease Detection of Pre and Post-Harvest Plant Diseases: Recent Developments and Future.- Comparative Study Based on Lung Cancer with Covid-19 Using Deep Learning and Machine Learning.- Low-cost Data Acquisition System for Electric Vehicles.- Machine Learning based Robotic-Assisted Upper Limb Rehabilitation Therapies: A Review.- Performance Analysis of Classic LEACH vs CC-LEACH.- Anomaly Detection in the Course Evaluation Process.- Self Attention Based Efficient U-Net for Crack Segmentation.- Lung Carcinoma Detection from CT Images Using Image Segmentation.- A Deep Learning Based Human Activity Recognition System for Monitoring the Elderly People.- Tweets Classification on the Base of Sentiments using Deep Learning.

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Book SynopsisThis book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.Table of ContentsIntroduction.- Survey of Image Co-segmentation.- Mathematical Background.- Co-segmentation using a Classification Framework.- Use of Maximum Common Subgraph Matching.- Maximally Occurring Common Subgraph Matching.- Co-segmentation using Graph Convolutional Neural Network.- Use of a Conditional Siamese Convolutional Network.- Few-shot Learning for Co-segmentation.- Conclusions.

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Book SynopsisAttack and Defense.- Domain Adaptation for Cross-Device Profiled ML Side-Channel Attacks..- Find the Clasp of the Chain: Efficiently Locating Cryptographic Procedures in SoC Secure Boot by Semi-automated Side-Channel Analysis..- Full-phase distributed quantum impossible differential cryptanalysis..- ProverNG: Efficient Verification of Compositional Masking for Cryptosystem’s Side-Channel Security..- POWERPOLY: Multilingual Program Analysis with the Aid of WebAssembly..- Not only spatial, but also spectral: Unnoticeable backdoor attack on 3D point clouds..- Permutation-Based Cryptanalysis of the SCARF Block Cipher and Its Randomness Evaluation..- Secure and Scalable TLB Partitioning Against Timing Side-Channel Attacks..- Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories..- Vulnerability Analysis..- Towards Efficient C/C++ Vulnerability Impact Assessment in Package Management Systems..- AugGP-VD: A smart contract vulnerability detection approach based on augmented graph convolutional networks and pooling..- VULDA: Source Code Vulnerability Detection via Local Dependency Context Aggregation on Vulnerability-aware Code Mapping Graph..- KVT-Payload: Knowledge Graph-Enhanced Hierarchical Vulnerability Traffic Payload Generation..- Construction and Application of Vulnerability Intelligence Ontology under Vulnerability Management Perspective..- Anomaly Detection..- Speaker Inference Detection Using Only Text..- DTGAN: Diverse-Task Generative Adversarial Networks for Intrusion Detection Systems Against Adversarial Examples..- ConComFND: Leveraging Content and Comment Information for Enhanced Fake News Detection..-Transferable Adversarial Attacks in Object Detection: Leveraging Ensemble Features and Gradient Variance Minimization..- VAE-BiLSTM: A Hybrid Model for DeFi Anomaly Detection Combining VAE and BiLSTM..- FluxSketch: A Sketch-based Solution for Long-Term Fluctuating Key Flow Detection..- RustGuard: Detecting Rust Data Leak Issues with Context-Sensitive Static Taint Analysis..- Secure Guard: A Semantic-Based Jailbreak Prompt Detection Framework for Protecting Large Language Models..- Traffic Classification..- FCAL: An Asynchronous Federated Contrastive Semi-Supervised Learning Approach for Network Traffic Classification..- TetheGAN: A GAN-Based Synthetic Mobile Tethering Traffic Generating Framework..- SPTC: Signature-based Cross-protocol Encrypted Proxy Traffic Classification Approach..- Multi-modal Datagram Representation with Spatial-Temporal State Space Models and Inter-flow Contrastive Learning for Encrypted Traffic Classification..- FlowGraphNet: Efficient Malicious Traffic Detection via Graph Construction..- CascadeGen: A Hybrid GAN-Diffusion Framework for Controllable and Protocol-Compliant Synthetic Network Traffic Generation..- Steganography and Watermarking..- Towards High-Capacity Provably Secure Steganography via Cascade Sampling..- When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-box Setting..- Robust Reversible Watermarking for 3D Models Based on Auto Diffusion Function.

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Book Synopsis.- Dynamic spectrum sharing scheme for anti-malicious user's queries based on blockchain..- Internet of Vehicle Privacy Protection Solution Based on Blockchain and Zero Knowledge Proof..- A New Asymmetric Three-Party Key Agreement Protocol Based on Secure Multiparty Computation..- TCAu:Time-Controllable Air-Space-Ground Authentication..- A NIZK-Based Linkable Ring Signature Scheme for Anonymous Blockchain System on Lattice..- CP-ABZP: A CP-ABE Cross-chain Privacy Protection Scheme Combined with Zero-Knowledge Proof..- A Novel Dual-layer Access Control Model based on Blockchain for the Case-involved Property Management..- MUSES: Secure Multi-User Encrypted Speech Search in Cloud-Edge-End Environments..- Efficient Group Key Management for Vehicle Ad Hoc Networks..- Privacy-Preserving Machine Learning Using Functional Encryptions for Multiple Models with Constant Ciphertext..- A Privacy-preserving Delegable Auditing Scheme with Edge-assisted Deduplication in IoT..- A Multi-KGC Certificateless Anonymous Cloud Sharing Scheme with Threshold-based Traceability..- A Differential Privacy Approach to Optimization for Protecting Large Models..- Unifying Physical-Layer Security and Post-Quantum Cryptography: An Optimized Authentication Encryption Protocol..- Anonymous Batch Authentication and Key Agreement for Internet of Things..- A lightweight Privacy-Preserving kNN Query Framework via Secret Sharing in Cloud Computing..- BiLSTMTimeGAN: Enhancing Side-Channel Attacks through Temporal Data Augmentation..- An encrypted anomaly traffic detection method integrating adversarial training and multi-scale contrastive learning..- MGtest: Python Coverage-Guided Model-Level Fuzzing for DL frameworks..- Collaborative Physical Layer Authentication for Vehicular Networks based on Dempster-Shafer Evidence Theory..- A Survey of Kernel Fuzzing..- Federated Learning against Dynamic Mixed Poisoning Attack and its Defense..- Robust Fake Image Detection via Incremental Training of Siamese Neural Networks..- Advancing Deepfake Detection through Head Pose Estimation on New-Generation Datasets..- A Verifiable and Privacy-Preserving Credit Risk Prediction under Secure Neural Network Scheme..- Security Assessment in Optical Networks: A Path Graph-based Framework for Attack-Induced Fault Diagnosis..- Machine Learning Model Construction and Multi-Dimensional Evaluation for Pop-up Text Sentiment Analysis..- Enhancing Double Ratchet Algorithm with Pre-Shared Key Mechanism and Optimal KDF Selection.

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Book SynopsisChapter 1. Introduction to Multimodal Generative AI.- Chapter 2. ChatGPT and BERT: Comparative Analysis of Various Natural Language Processing Applications.- Chapter 3. Large Language Model on Multi-Modal Data.- Chapter 4.  Adaptive Learning Technologies: Navigating the Road from Hype to Reality.- Chapter 5. Generative Artificial Intelligence in Visual Content: A Review of the Influence on Consumer Perception and Perspective.- Chapter 6. Text-to-Image Synthesis: Techniques and Applications.- Chapter 7. Image-to-Text Generation: Bridging Visual and Linguistic Worlds.- Chapter 8. Sustainability in the Metaverse: Challenges, Implications, and Potential Solutions.- Chapter 9. Transcendent Artificial Intelligence in Education.- Chapter 10. Chat GPT in Academia and Research - A Comprehensive Review of Integrating AI in Higher Education.- Chapter 11. Exploring Multimodal Hate Speech Detection Using Machine Learning and Deep Learning Models.- Chapter 12. Multimodal Generative AI for People with Disabilities.- Chapter 13.                Single-Modality to Multimodality: The Evolutionary Trajectory of Artificial Intelligence in Integrating Diverse Data Streams for Enhanced Cognitive Capabilities.- Chapter 14. Interfacing Multimodal AI with IoT: Unlocking New Frontiers.-Chapter 15. Enhancing Safety and Reliability in VANETs for Autonomous Vehicles by M-XAI (Multi Model- Explainable-AI) .- Chapter 16. Future Directions In Multimodal Genrative AI.

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Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisContextual Feature-Based Medical Visual Question Answering Aided by Learnable Matrix.- ImgQuant: Towards adversarial defense with robust boundary via dual-image quantization.- Swelling-ViT: Rethink Data-efficient Vision Transformer from Locality.- Target-Specific Domain Adaptation via Geometry-Correlation Prediction for Point Cloud.- Dual-stream Network of Vision Mamba and CNN with Auto-scaling for Remote Sensing Image Segmentation.- PRM: A Pixel-Region-Matching Approach for Fast Video Object Segmentation.- A Novel Combined GAN for Defects Generation using Masking Mechanisms.- Semi-supervised lightweight fabric defect detection.- Semi-adaptive Synergetic Two-way Pseudoinverse Learning System.- Invariant Risk Minimization Augmentation for Graph Contrastive Learning.- Enhancing Fast Adversarial Training with Learnable Adversarial Perturbations.- DTAFORMER: Directional Time Attention Transformer For Long-Term Series Forecasting.- Unpaired Multi-scenario Sketch Synthesis via Texture Enh

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