Computer Vision – ECCV 2022

Table of Contents

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   Book Overview
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   Chapter 1 A Simple Approach and Benchmark for 21,000-Category Object Detection
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   Chapter 2 Knowledge Condensation Distillation
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   Chapter 3 Reducing Information Loss for Spiking Neural Networks
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   Chapter 4 Masked Generative Distillation
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   Chapter 5 Fine-grained Data Distribution Alignment for Post-Training Quantization
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   Chapter 6 Learning with Recoverable Forgetting
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   Chapter 7 Efficient One Pass Self-distillation with Zipf’s Label Smoothing
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   Chapter 8 Prune Your Model Before Distill It
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    Chapter 9 Deep Partial Updating: Towards Communication Efficient Updating for On-Device Inference
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    Chapter 10 Patch Similarity Aware Data-Free Quantization for Vision Transformers
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    Chapter 11 L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
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    Chapter 12 Streaming Multiscale Deep Equilibrium Models
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    Chapter 13 Symmetry Regularization and Saturating Nonlinearity for Robust Quantization
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    Chapter 14 SP-Net: Slowly Progressing Dynamic Inference Networks
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    Chapter 15 Equivariance and Invariance Inductive Bias for Learning from Insufficient Data
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    Chapter 16 Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance
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    Chapter 17 Event Neural Networks
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    Chapter 18 EdgeViTs: Competing Light-Weight CNNs on Mobile Devices with Vision Transformers
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    Chapter 19 PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators
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    Chapter 20 Disentangled Differentiable Network Pruning
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    Chapter 21 IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit Detectors
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    Chapter 22 Learning to Weight Samples for Dynamic Early-Exiting Networks
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    Chapter 23 AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
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    Chapter 24 Adaptive Token Sampling for Efficient Vision Transformers
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    Chapter 25 Weight Fixing Networks
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    Chapter 26 Self-slimmed Vision Transformer
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    Chapter 27 Switchable Online Knowledge Distillation
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    Chapter 28 $$\ell _\infty $$-Robustness and Beyond: Unleashing Efficient Adversarial Training
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    Chapter 29 Multi-granularity Pruning for Model Acceleration on Mobile Devices
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    Chapter 30 Deep Ensemble Learning by Diverse Knowledge Distillation for Fine-Grained Object Classification
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    Chapter 31 Helpful or Harmful: Inter-task Association in Continual Learning
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    Chapter 32 Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
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    Chapter 33 SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
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    Chapter 34 Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning
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    Chapter 35 Network Binarization via Contrastive Learning
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    Chapter 36 Lipschitz Continuity Retained Binary Neural Network
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    Chapter 37 SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning
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    Chapter 38 Soft Masking for Cost-Constrained Channel Pruning
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    Chapter 39 Non-uniform Step Size Quantization for Accurate Post-training Quantization
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    Chapter 40 SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning
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    Chapter 41 Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously
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    Chapter 42 Towards Ultra Low Latency Spiking Neural Networks for Vision and Sequential Tasks Using Temporal Pruning
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    Chapter 43 Towards Accurate Network Quantization with Equivalent Smooth Regularizer