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020			_a9783030695385
024	7		_a10.1007/978-3-030-69538-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2021 EB
245	0	0	_aComputer Vision - ACCV 2020 : _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			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVIII, 715 páginas) _b284 ilustraciones, 278 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aImage Processing Computer Vision Pattern Recognition and Graphics ; _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	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _aVisión por ordenador _vCongresos y asambleas _9159793
650		7	_2embne _aReconocimiento de formas _vCongresos y asambleas _9152614
700	1		_aIshikawa, Hiroshi _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLiu, Cheng-Lin _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aPajdla, Tomas _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aShi, Jianbo _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-69538-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2021 _dz _eb _zSI