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Front Matter
Front Matter
Spatial and Channel Attention Modulated Network for Medical Image Segmentation
Medical image segmentation is a fundamental and challenge task in many computer-aided diagnosis and surgery systems, and attracts numerous research attention in computer vision and medical image processing fields. Recently, deep learning based ...
Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution
In recent years, convolutional neural networks (CNNs) have obtained promising results in single-image super-resolution (SR) for remote sensing images. However, most existing methods are inadequate for remote sensing image SR due to the high ...
Unsupervised Multispectral and Hyperspectral Image Fusion with Deep Spatial and Spectral Priors
Hyperspectral (HS) imaging is a promising imaging modality, which can simultaneously acquire various bands of images of the same scene and capture detailed spectral distribution helping for numerous applications. However, existing HS imaging ...
G-GCSN: Global Graph Convolution Shrinkage Network for Emotion Perception from Gait
Recently, emotion recognition through gait, which is more difficult to imitate than other biological characteristics, has aroused extensive attention. Although some deep-learning studies have been conducted in this field, there are still two ...
Cell Detection and Segmentation in Microscopy Images with Improved Mask R-CNN
Analyzing and elucidating the attributes of cells and tissues with an observed microscopy image is a fundamental task in both biological research and clinical practice, and automation of this task to develop computer aided system based on image ...
BdSL36: A Dataset for Bangladeshi Sign Letters Recognition
Bangladeshi Sign Language (BdSL) is a commonly used medium of communication for the hearing-impaired people in Bangladesh. A real-time BdSL interpreter with no controlled lab environment has a broad social impact and an interesting avenue of ...
A Weakly Supervised Convolutional Network for Change Segmentation and Classification
Fully supervised change detection methods require difficult to procure pixel-level labels, while weakly supervised approaches can be trained with image-level labels. However, most of these approaches require a combination of changed and unchanged ...
Front Matter
Visible and Thermal Camera-Based Jaywalking Estimation Using a Hierarchical Deep Learning Framework
Jaywalking is an abnormal pedestrian behavior which significantly increases the risk of road accidents. Owing to this risk, autonomous driving applications should robustly estimate the jaywalking pedestrians. However, the task of robustly ...
Towards Locality Similarity Preserving to 3D Human Pose Estimation
Estimating 3D human pose from an annotated or detected 2D pose in a single RGB image is a challenging problem. A successful way to address this problem is the example-based approach. The existing example-based approaches often calculate a global ...
Iterative Self-distillation for Precise Facial Landmark Localization
In this paper, we propose a novel training method to improve the precision of facial landmark localization. When a facial landmark localization method is applied to a facial video, the detected landmarks occasionally jitter, whereas the face ...
Multiview Similarity Learning for Robust Visual Clustering
Multiview similarity learning aims to measure the neighbor relationship between each pair of samples, which has been widely used in data mining and presents encouraging performance on lots of applications. Nevertheless, the recent existing ...
Index Terms
- Computer Vision – ACCV 2020 Workshops: 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers