000 06284nam a22006375i 4500
001 978-3-031-20083-0
003 DE-He213
005 20240730170554.0
007 cr nn 008mamaa
008 221102s2022 sz | s |||| 0|eng d
020 _a9783031200830
_9978-3-031-20083-0
024 7 _a10.1007/978-3-031-20083-0
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQV
_2thema
082 0 4 _a006.37
_223
245 1 0 _aComputer Vision - ECCV 2022
_h[electronic resource] :
_b17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XI /
_cedited by Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2022.
300 _aLVI, 745 p. 231 illus., 215 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13671
505 0 _aA 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.
520 _aThe 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.
650 0 _aComputer vision.
_994389
650 0 _aComputers.
_98172
650 0 _aComputer engineering.
_910164
650 0 _aComputer networks .
_931572
650 0 _aMachine learning.
_91831
650 1 4 _aComputer Vision.
_994393
650 2 4 _aComputing Milieux.
_955441
650 2 4 _aComputer Engineering and Networks.
_994395
650 2 4 _aComputer Engineering and Networks.
_994395
650 2 4 _aMachine Learning.
_91831
700 1 _aAvidan, Shai.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_994398
700 1 _aBrostow, Gabriel.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_994399
700 1 _aCissé, Moustapha.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_994402
700 1 _aFarinella, Giovanni Maria.
_eeditor.
_0(orcid)
_10000-0002-6034-0432
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_994403
700 1 _aHassner, Tal.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_994404
710 2 _aSpringerLink (Online service)
_994406
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031200823
776 0 8 _iPrinted edition:
_z9783031200847
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13671
_923263
856 4 0 _uhttps://doi.org/10.1007/978-3-031-20083-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cELN
999 _c87055
_d87055