Computer Vision – ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XI / edited by Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner.

Format:

Book

Author/Creator:

Avidan, Shai, author.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13671

Language:

English

Subjects (All):

Computer vision.

Computers.

Computer engineering.

Computer networks.

Machine learning.

Computer Vision.

Computing Milieux.

Computer Engineering and Networks.

Machine Learning.

Local Subjects:

Computer Vision.

Computing Milieux.

Computer Engineering and Networks.

Machine Learning.

Physical Description:

1 online resource (801 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 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; object recognition; motion estimation.

Contents:

A Simple Approach and Benchmark for 21,000-Category Object Detection

Knowledge Condensation Distillation

Reducing Information Loss for Spiking Neural Networks

Masked Generative Distillation

Fine-Grained Data Distribution Alignment for Post-Training Quantization

Learning with Recoverable Forgetting

Efficient One Pass Self-Distillation with Zipf’s Label Smoothing

Prune Your Model before Distill It

Deep Partial Updating: Towards Communication Efficient Updating for On-Device Inference

Patch Similarity Aware Data-Free Quantization for Vision Transformers

L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training

Streaming Multiscale Deep Equilibrium Models

Symmetry Regularization and Saturating Nonlinearity for Robust Quantization

SP-Net: Slowly Progressing Dynamic Inference Networks

Equivariance and Invariance Inductive Bias for Learning from Insufficient Data

Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance

Event Neural Networks

EdgeViTs: Competing Light-Weight CNNs on Mobile Devices with Vision Transformers

PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators

Disentangled Differentiable Network Pruning

IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit Detectors

Learning to Weight Samples for Dynamic Early-Exiting Networks

AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

Adaptive Token Sampling for Efficient Vision Transformers

Weight Fixing Networks

Self-Slimmed Vision Transformer

Switchable Online Knowledge Distillation

ℓ∞-Robustness and Beyond: Unleashing Efficient Adversarial Training

Multi-Granularity Pruning for Model Acceleration on Mobile Devices

Deep Ensemble Learning by Diverse Knowledge Distillation for Fine-Grained Object Classification

Helpful or Harmful: Inter-Task Association in Continual Learning

Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies

SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks

Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning

Network Binarization via Contrastive Learning

Lipschitz Continuity Retained Binary Neural Network

SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning

Soft Masking for Cost-Constrained Channel Pruning

Non-uniform Step Size Quantization for Accurate Post-Training Quantization

SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning

Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously

Towards Ultra Low Latency Spiking Neural Networks for Visionand Sequential Tasks Using Temporal Pruning

Towards Accurate Network Quantization with Equivalent Smooth Regularizer.

Other Format:

Print version: Avidan, Shai Computer Vision - ECCV 2022

ISBN:

9783031200830

3031200837