Description
Book SynopsisPart 1 :Artificial Intelligence:
.- SRG-Net: Semantic Relation-Guided Network for Commonsense Video Captioning.
.- EAANet: Edge-Aware Attention Network for Real-Time Road Scene Understanding.
.- Learning A Decomposition-Driven Two Stages Unfolding Artifact Removal Network for Compressed Images.
.- Martingale-Based Skin Lesion Segmentation from Dermoscopic Images.
.- Research on Adaptive Multi-layer Multi-pass Welding Technology for Medium-Thick Plates.
.- M³E: Mixture of Multi-scale Multi-modal Experts for Time Series Forecasting.
.- PoseCLR Bridging 2D and 3D Pose Representations via Contrastive Learning for Action Recognition.
.- Art3D-Fusion: A Hybrid Framework for Visual Synthesis with Artistic Control.
.- Lesion Localization Prior-Driven Few-Shot Learning for Branch Atheromatous Disease Diagnosis.
.- Deep Multi-Sentence Aligned Cross-Modal Retrieval.
.- Single-Layer Denoising Taylorformer for UAV Nighttime Tracking.
.- Position-Aware Text-to-Image Generation with Efficient Controllability.
.- Introducing DINOv2 for Medical Image Boundary Tracking.
.- Adaptive Pruning and Cross-Domain Feature Fusion for Robust Object Tracking.
.- Data Leakage Detection in Large Vision-Language Models via Multimodal Perturbation.
.- A Novel Dual-Branch Cross-Attention Transformer Network for Low-Dose CT Denoising.
.- TCGFNet: Multi-Scale Transformer-Convolution with Geometry-Guided Feedback for Robust Point Cloud Denoising.
.- Adversarial Iterative Pre-Enactment Framework for Air Combat Based on Mental Simulation Theory.
.- SA-Pillar: Structure-Aware Feature Learning for Real-Time 3D Object Detection.
.- Knowledge-aware Intent Subgraph Learning for Recommendation.
.- PF-DETR:Enhanced DETR with Pre-Encoded Feature Fusion for Small and Multi-Scale Object Detection in UAV Imagery.
.- Selective Labeling for 3D Shape Label Transfer based on Local-Global Features.
.- Part 2 :Biological and Medical Image Processing:
.- MAA-Net: A Multi-Attention Aggregation Network for Segmentation of Key Structures in Microvascular Decompression.
.- Contrastive Hierarchical Graph based Multiple Instance Learning for Fundus Screening
.- Polyp Segmentation based on Edge Guidance.
.- A Deep Unfolding based on U-Net Graph-Guided Hybrid Regularization method for Bioluminescence Tomography.
.- CMambaR: Cardiac Phase Embedded Vision Mamba for Accelerating Cardiac MRI Reconstruction.
.- SC-DSE-nnUNet: An Efficient Hippocampus MRI Segmentation Method.
.- Spatiotemporal Feature Fusion for Glioblastoma Recurrence Prediction Using Mamba-Based Dual-Stream Framework.
.- Automatic and Fast Segmentation of Cochlear Implant-Induced Artifacts in MR Images Using Deep Learning.
.- Part 3 :Color and Multispectral Processing:
.- End-to-End Diffusion Models with Physics Priors for Enhanced Spectral Super-Resolution.
.- Asymmetric Dual-Teacher Guided Knowledge Distillation for HSI-SR with Reconstructed Features.
.- Gradient-based multi-focus image fusion with focus-aware saliency enhancement.
.- OME-Net: Optimization-inspired Multi-domain Enhanced Network for Image Compressed Sensing Reconstruction.
.- Part 4 :Compression, Transmission, Retrieval:
.- MARSNet: Scalable Deep Coding of LiDAR Point Clouds via Multimodal and Residual Learning.
.- Accelerating Learned Video Compression via Low-Resolution Representation Learning.
.- Optical Flow-driven Fast CU Partition for Inter Prediction in Versatile Video Coding.
.- Semantic Maintained Video Compression by Background Blurring in Surveillance Scenarios.
.- Learning Based Fast Coding Unit Decision for Video-based Point Cloud Compression.
.- Part 5 :Computational Imaging:
.- Leveraging a Dual-Learning Methodology Based on Degradation Modeling and Fractional Fourier Image Transformer for Light Field Image Super-Resolution.
.- Video Stabilization Based on MeshFlow Motion Model in Dynamic and Complex Scenes.
Dual-Edge Consistency Constrained Unfolding Network for Depth Map Super-Resolution.
.- Part 6 :Computer Graphics and Visualization:
.- Isotropic Remeshing with Inter-Angle Optimization.
.- AlignMR: Design of a Home Yoga Self Learning System Based on MR Technology.
.- Bi-IRNet: A Transformer-based Binaural Impulse Response Generation Guidance Model.
