A Unified Transformer Framework for Group-Based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection

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成果类型：

期刊论文

作者：

Su, Yukun;Deng, Jingliang;Sun, Ruizhou;Lin, Guosheng;Su, Hanjing;...

通讯作者：

Lin, GS;Wu, QY

作者机构：

[Su, Yukun; Deng, Jingliang; Sun, Ruizhou] South China Univ Technol, Sch Software Engn, Key Lab Big Data & Intelligent Robot, Minist Educ, Guangzhou 510006, Peoples R China.

[Lin, Guosheng] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.

[Su, Hanjing] Tencent, Shenzhen 518000, Peoples R China.

[Wu, Qingyao] South China Univ, Sch Software Engn, Pazhou Lab, Guangzhou 510006, Peoples R China.

[Wu, Qingyao] Peng Cheng Lab, Shenzhen 518066, Peoples R China.

通讯机构：

[Wu, QY ] S

[Lin, GS ] N

Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore.

South China Univ, Sch Software Engn, Pazhou Lab, Guangzhou 510006, Peoples R China.

语种：

英文

关键词：

Co-object;long-range dependency;transformer;activation

期刊：

IEEE Transactions on Multimedia

ISSN：

1520-9210

年：

2024

卷：

页码：

313-325

DOI：

10.1109/TMM.2023.3264883

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62272172) 10.13039/501100021171-Basic and Applied Basic Research Foundation of Guangdong Province (Grant Number: 2023A1515012920) 10.13039/501100013111-Tip-Top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program (Grant Number: 2019TQ05X200) 2022 Tencent Wechat Rhino-Bird Focused Research Program (Grant Number: RBFR2022008) Major Key Project of PCL (Grant Number: PCL2021A09) 10.13039/501100001381-National Research Foundation Singapore (Grant Number: AISG-RP-2018-003) MOE AcRF Tier-1 Research (Grant Number: RG95/20)

机构署名：

本校为通讯机构

摘要：

Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researchers focus on co-segmentation (CoS), co-saliency detection (CoSD) and video salient object detection (VSOD) to discover the co-occurrent objects. However, previous approaches design different networks for these similar tasks separately, and they are difficult to apply to each other. Besides, they fail to take full advantage of the cues among inter- and intra-feature within a group of images. In this paper, we introduce a unified fr...

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A Unified Transformer Framework for Group-Based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection

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