000 06571nam a22006375i 4500
001 978-3-030-69538-5
003 DE-He213
005 20240730164946.0
007 cr nn 008mamaa
008 210224s2021 sz | s |||| 0|eng d
020 _a9783030695385
_9978-3-030-69538-5
024 7 _a10.1007/978-3-030-69538-5
_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 - ACCV 2020
_h[electronic resource] :
_b15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, Revised Selected Papers, Part IV /
_cedited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVIII, 715 p. 284 illus., 278 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 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v12625
505 0 _aDeep Learning for Computer Vision -- In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization -- Exploiting Transferable Knowledge for Fairness-aware Image Classification -- Introspective Learning by Distilling Knowledge from Online Self-explanation -- Hyperparameter-Free Out-of-Distribution Detection Using Cosine Similarity -- Meta-Learning with Context-Agnostic Initialisations -- Second Order enhanced Multi-glimpse Attention in Visual Question Answering -- Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection -- Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes -- Part-aware Attention Network for Person Re-Identification -- Image Captioning through Image Transformer -- Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Acceleration -- Learn more, forget less: Cues from human brain -- Knowledge Transfer Graph for Deep Collaborative Learning -- Regularizing Meta-Learning via Gradient Dropout -- Vax-a-Net: Training-time Defence Against Adversarial Patch Attacks -- Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed -- Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation -- Double Targeted Universal Adversarial Perturbations -- Adversarially Robust Deep Image Super-Resolution using Entropy Regularization -- Online Knowledge Distillation via Multi-branch Diversity Enhancement -- Rotation Equivariant Orientation Estimation for Omnidirectional Localization -- Contextual Semantic Interpretability -- Few-Shot Object Detection by Second-order Pooling -- Depth-Adapted CNN for RGB-D cameras -- Generative Models for Computer Vision -- Over-exposure Correction via Exposure and Scene Information Disentanglement -- Novel-View Human Action Synthesis -- Augmentation Network for Generalised Zero-Shot Learning -- Local Facial Makeup Transfer via Disentangled Representation -- OpenGAN: Open Set Generative Adversarial Networks -- CPTNet: Cascade Pose Transform Network for Single Image Talking Head Animation -- TinyGAN: Distilling BigGAN for Conditional Image Generation -- A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings -- RF-GAN: A Light and Reconfigurable Network for Unpaired Image-to-Image Translation -- GAN-based Noise Model for Denoising Real Images -- Emotional Landscape Image Generation Using Generative Adversarial Networks -- Feedback Recurrent Autoencoder for Video Compression -- MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network -- DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution -- dpVAEs: Fixing Sample Generation for Regularized VAEs -- MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network -- EvolGAN: Evolutionary Generative Adversarial Networks -- Sequential View Synthesis with Transformer.
520 _aThe six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.
650 0 _aComputer vision.
_986840
650 0 _aArtificial intelligence.
_93407
650 0 _aComputer engineering.
_910164
650 0 _aComputer networks .
_931572
650 0 _aPattern recognition systems.
_93953
650 0 _aApplication software.
_986843
650 1 4 _aComputer Vision.
_986844
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Engineering and Networks.
_986846
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aComputer and Information Systems Applications.
_986848
700 1 _aIshikawa, Hiroshi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_929891
700 1 _aLiu, Cheng-Lin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986850
700 1 _aPajdla, Tomas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986852
700 1 _aShi, Jianbo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986853
710 2 _aSpringerLink (Online service)
_986856
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030695378
776 0 8 _iPrinted edition:
_z9783030695392
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v12625
_986857
856 4 0 _uhttps://doi.org/10.1007/978-3-030-69538-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cELN
999 _c86015
_d86015