| TÃtulo : |
15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part IV |
| Tipo de documento: |
documento electrónico |
| Autores: |
Ishikawa, Hiroshi, ; Liu, Cheng-Lin, ; Pajdla, Tomas, ; Shi, Jianbo, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2021 |
| Número de páginas: |
XVIII, 715 p. 284 ilustraciones, 278 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-69538-5 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Visión por computador Inteligencia artificial IngenierÃa Informática Red de computadoras Sistemas de reconocimiento de patrones Software de la aplicacion IngenierÃa Informática y Redes Reconocimiento de patrones automatizado Aplicaciones informáticas y de sistemas de información |
| Ãndice Dewey: |
006.37 Visión artificial |
| Resumen: |
El conjunto de seis volúmenes de LNCS 12622-12627 constituye las actas de la 15.ª Conferencia asiática sobre visión artificial, ACCV 2020, celebrada en Kioto (Japón) en noviembre/diciembre de 2020.* El total de 254 contribuciones se revisó cuidadosamente y se seleccionó de 768 presentaciones durante dos rondas de revisión y mejora. Los artÃculos se centran en los siguientes temas: Parte I: visión artificial en 3D; segmentación y agrupamiento Parte II: visión de bajo nivel, procesamiento de imágenes; movimiento y seguimiento Parte III: reconocimiento y detección; optimización, métodos estadÃsticos y aprendizaje; visión robótica Parte IV: aprendizaje profundo para visión artificial, modelos generativos para visión artificial Parte V: rostro, pose, acción y gesto; análisis de vÃdeo y reconocimiento de eventos; análisis de imágenes biomédicas Parte VI: aplicaciones de la visión artificial; visión para X; conjuntos de datos y análisis de rendimiento *La conferencia se celebró de forma virtual. |
| Nota de contenido: |
Deep 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. |
| En lÃnea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part IV [documento electrónico] / Ishikawa, Hiroshi, ; Liu, Cheng-Lin, ; Pajdla, Tomas, ; Shi, Jianbo, . - 1 ed. . - [s.l.] : Springer, 2021 . - XVIII, 715 p. 284 ilustraciones, 278 ilustraciones en color. ISBN : 978-3-030-69538-5 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Visión por computador Inteligencia artificial IngenierÃa Informática Red de computadoras Sistemas de reconocimiento de patrones Software de la aplicacion IngenierÃa Informática y Redes Reconocimiento de patrones automatizado Aplicaciones informáticas y de sistemas de información |
| Ãndice Dewey: |
006.37 Visión artificial |
| Resumen: |
El conjunto de seis volúmenes de LNCS 12622-12627 constituye las actas de la 15.ª Conferencia asiática sobre visión artificial, ACCV 2020, celebrada en Kioto (Japón) en noviembre/diciembre de 2020.* El total de 254 contribuciones se revisó cuidadosamente y se seleccionó de 768 presentaciones durante dos rondas de revisión y mejora. Los artÃculos se centran en los siguientes temas: Parte I: visión artificial en 3D; segmentación y agrupamiento Parte II: visión de bajo nivel, procesamiento de imágenes; movimiento y seguimiento Parte III: reconocimiento y detección; optimización, métodos estadÃsticos y aprendizaje; visión robótica Parte IV: aprendizaje profundo para visión artificial, modelos generativos para visión artificial Parte V: rostro, pose, acción y gesto; análisis de vÃdeo y reconocimiento de eventos; análisis de imágenes biomédicas Parte VI: aplicaciones de la visión artificial; visión para X; conjuntos de datos y análisis de rendimiento *La conferencia se celebró de forma virtual. |
| Nota de contenido: |
Deep 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. |
| En lÃnea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
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15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part IV
