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Book Synopsis

Practical 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.

Computer Vision ECCV 2024 Workshops

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    A Paperback by Alessio Del Bue

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

      Publisher: Springer
      Publication Date: 6/1/2025
      ISBN13: 9783031938054, 978-3031938054
      ISBN10: 3031938054
      Also in:
      Image processing

      Description

      Book Synopsis

      Practical 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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