activity
20182023
most citedProgressively Normalized Self-Attention Network for Video Polyp Segmentation

140 citations · 365 across the 16 of their papers we have counts for

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Showing 2021Show all

8 papers · 1 filter

cs.CV2021

Specificity-preserving RGB-D Saliency Detection

Tao Zhou, Deng-Ping Fan, Geng Chen +2

Salient object detection (SOD) on RGB and depth images has attracted more and more research interests, due to its effectiveness and the fact that depth cues can now be conveniently…

cs.CV2021★ 25 cited

Context-aware Cross-level Fusion Network for Camouflaged Object Detection

Yujia Sun, Geng Chen, Tao Zhou +2

Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged object…

cs.CV2021★ 140 cited

Progressively Normalized Self-Attention Network for Video Polyp Segmentation

Ge-Peng Ji, Yu-Cheng Chou, Deng-Ping Fan +4

Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs can n…

eess.IV2021★ 4 cited

Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction

Chun-Mei Feng, Zhanyuan Yang, Geng Chen +2

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging h…

cs.CV2021★ 11 cited

Learning Synergistic Attention for Light Field Salient Object Detection

Yi Zhang, Geng Chen, Qian Chen +5

We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with…

cs.CV2021

Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration

Hong-Bo Bi, Zi-Qi Liu, Kang Wang +3

Saliency detection based on the complementary information from RGB images and depth maps has recently gained great popularity. In this paper, we propose Complementary Attention and…