Search Results for author: Ge-Peng Ji

Found 12 papers, 10 papers with code

Full-Duplex Strategy for Video Object Segmentation

1 code implementation ICCV 2021 Ge-Peng Ji, Deng-Ping Fan, Keren Fu, Zhe Wu, Jianbing Shen, Ling Shao

Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues.

Salient Object Detection Semantic Segmentation +2

Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection

1 code implementation5 Jul 2021 Wenbo Zhang, Ge-Peng Ji, Zhuo Wang, Keren Fu, Qijun Zhao

To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy.

RGB-D Salient Object Detection Salient Object Detection

Guidance and Teaching Network for Video Salient Object Detection

no code implementations21 May 2021 Yingxia Jiao, Xiao Wang, Yu-Cheng Chou, Shouyuan Yang, Ge-Peng Ji, Rong Zhu, Ge Gao

Owing to the difficulties of mining spatial-temporal cues, the existing approaches for video salient object detection (VSOD) are limited in understanding complex and noisy scenarios, and often fail in inferring prominent objects.

Salient Object Detection Video Salient Object Detection

Progressively Normalized Self-Attention Network for Video Polyp Segmentation

2 code implementations18 May 2021 Ge-Peng Ji, Yu-Cheng Chou, Deng-Ping Fan, Geng Chen, Huazhu Fu, Debesh Jha, Ling Shao

Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features.

Camouflaged Object Segmentation with Distraction Mining

no code implementations CVPR 2021 Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, Deng-Ping Fan

In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature.

Camouflaged Object Segmentation Semantic Segmentation

Concealed Object Detection

1 code implementation20 Feb 2021 Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, Ling Shao

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background.

Camouflaged Object Segmentation

Light Field Salient Object Detection: A Review and Benchmark

1 code implementation10 Oct 2020 Keren Fu, Yao Jiang, Ge-Peng Ji, Tao Zhou, Qijun Zhao, Deng-Ping Fan

Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved.

Object Detection Saliency Detection +1

Siamese Network for RGB-D Salient Object Detection and Beyond

2 code implementations26 Aug 2020 Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao, Jianbing Shen, Ce Zhu

Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.

Ranked #2 on RGB-D Salient Object Detection on SIP (using extra training data)

RGB-D Salient Object Detection Salient Object Detection +1

Re-thinking Co-Salient Object Detection

2 code implementations7 Jul 2020 Deng-Ping Fan, Tengpeng Li, Zheng Lin, Ge-Peng Ji, Dingwen Zhang, Ming-Ming Cheng, Huazhu Fu, Jianbing Shen

CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images.

Co-Salient Object Detection Salient Object Detection

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

3 code implementations13 Jun 2020 Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, Ling Shao

To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.

Ranked #3 on Camouflaged Object Segmentation on CAMO (using extra training data)

Camouflaged Object Segmentation Camouflage Segmentation +1

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