Description

Book Synopsis

.- TONO: a synthetic dataset for face image compliance to ISO/ICAO standard.
.- mproving Post-Earthquake Crack Detection using Semi-Synthetic Gener ated Images.
.- DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition.
.- Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis.
.- RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models.
.- NeRFmentation: NeRF-based Augmentation for Monocular Depth Estima tion.
.- Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition.
.- Time-Resolved MNIST Dataset for Single-Photon Recognition.
.- NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Hu man Pose Estimation in Top-View Fisheye Images.
.- Training and Benchmarking Leukocyte Sub-types Classification Methods with Synthetic Images.
.- DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling.
.- Contextual Knowledge Pursuit for Faithful Visual Synthesis.
.- SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models.
.- Diffusion-based Synthetic Dataset Generation for Egocentric 3D Human Pose Estimation.
.- BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabil ities in Pretrained Diffusion Models.
.- A CycleGAN Model to Synthesize Missing and Unpaired MRI Sequences for Under-Represented Multiple Sclerosis Lesions.
.- The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition.
.- DreamTexture: High-Fidelity Synthetic 3D Data Generation through De coupled Geometry and Texture Synthesis.
.- Control+Shift: Generating Controllable Distribution Shifts.
.- Comparative Analysis of Synthetic and Real Melanoma Images in AI-Driven Diagnosis.
.- How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recog nition.
.- Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization.
.- FABRIC: Personalizing Diffusion Models with Iterative Feedback.
.- TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection.

Computer Vision ECCV 2024 Workshops

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    A Paperback by Alessio Del Bue

    15 in stock


      View other formats and editions of Computer Vision ECCV 2024 Workshops by Alessio Del Bue

      Publisher: Springer
      Publication Date: 06/06/2025
      ISBN13: 9783031919060, 978-3031919060
      ISBN10:
      Also in:
      Computing Image processing

      Description

      Book Synopsis

      .- TONO: a synthetic dataset for face image compliance to ISO/ICAO standard.
      .- mproving Post-Earthquake Crack Detection using Semi-Synthetic Gener ated Images.
      .- DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition.
      .- Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis.
      .- RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models.
      .- NeRFmentation: NeRF-based Augmentation for Monocular Depth Estima tion.
      .- Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition.
      .- Time-Resolved MNIST Dataset for Single-Photon Recognition.
      .- NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Hu man Pose Estimation in Top-View Fisheye Images.
      .- Training and Benchmarking Leukocyte Sub-types Classification Methods with Synthetic Images.
      .- DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling.
      .- Contextual Knowledge Pursuit for Faithful Visual Synthesis.
      .- SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models.
      .- Diffusion-based Synthetic Dataset Generation for Egocentric 3D Human Pose Estimation.
      .- BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabil ities in Pretrained Diffusion Models.
      .- A CycleGAN Model to Synthesize Missing and Unpaired MRI Sequences for Under-Represented Multiple Sclerosis Lesions.
      .- The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition.
      .- DreamTexture: High-Fidelity Synthetic 3D Data Generation through De coupled Geometry and Texture Synthesis.
      .- Control+Shift: Generating Controllable Distribution Shifts.
      .- Comparative Analysis of Synthetic and Real Melanoma Images in AI-Driven Diagnosis.
      .- How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recog nition.
      .- Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization.
      .- FABRIC: Personalizing Diffusion Models with Iterative Feedback.
      .- TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection.

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