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Referring camouflaged object detection (Ref-COD) is a recently-proposed problem aiming to segment out specified camouflaged objects matched with a textual or visual reference.
Saliency filters: Contrast based filtering for salient region detection
Federico Perazzi, Philipp Krähenbühl, Yael Pritch, and Alexander Hornung · 2012
Earlier work this paper cites.
How to evaluate foreground maps?
Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal · 2014
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Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Structure-measure: A new way to evaluate foreground maps
Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji · 2017
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Knowledge-embedded representation learning for fine-grained image recognition
Tianshui Chen, Liang Lin, Riquan Chen, Yang Wu, and Xiaonan Luo · 2018
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Enhanced-alignment measure for binary foreground map evaluation
Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji · 2018
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Dock: Detecting objects by transferring common-sense knowledge
Krishna Kumar Singh, Santosh Divvala, Ali Farhadi, and Yong Jae Lee · 2018
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Anabranch network for camouflaged object segmentation
Trung-Nghia Le, Tam V Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugimoto · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Pestnet: An end-to-end deep learning approach for large-scale multi-class pest detection and classification
Liu Liu, Rujing Wang, Chengjun Xie, Po Yang, Fangyuan Wang, Sud Sudirman, and Wancai Liu · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Camouflaged object detection
Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao · 2020
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Integrating image captioning with rule-based entity masking
Aditya Mogadala, Xiaoyu Shen, and Dietrich Klakow · 2020
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Cross-modal knowledge reasoning for knowledge-based visual question answering
Jing Yu, Zihao Zhu, Yujing Wang, Weifeng Zhang, Yue Hu, and Jianlong Tan · 2020
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Rosita: Enhancing vision-and-language semantic alignments via cross-and intra-modal knowledge integration
Yuhao Cui, Zhou Yu, Chunqi Wang, Zhongzhou Zhao, Ji Zhang, Meng Wang, and Jun Yu · 2021
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Concealed object detection
Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Improving camouflaged object detection with the uncertainty of pseudo-edge labels
Nobukatsu Kajiura, Hong Liu, and Shin’ichi Satoh · 2021
Cited alongside, same era.
Uncertainty-aware joint salient object and camouflaged object detection
Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, and Yuchao Dai · 2021
Cited alongside, same era.
Simultaneously localize, segment and rank the camouflaged objects
Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping Fan · 2021
Cited alongside, same era.
Camouflaged object segmentation with distraction mining
Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, and Deng-Ping Fan · 2021
Cited alongside, same era.
Detecting camouflaged object in frequency domain
Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, and Shouhong Ding · 2022
Later among the works it cites.
Feature aggregation and propagation network for camouflaged object detection
Tao Zhou, Yi Zhou, Chen Gong, Jian Yang, and Yu Zhang · 2022
Later among the works it cites.
I can find you! boundary-guided separated attention network for camouflaged object detection
Hongwei Zhu, Peng Li, Haoran Xie, Xuefeng Yan, Dong Liang, Dapeng Chen, Mingqiang Wei, and Jing Qin · 2022
Later among the works it cites.
Cubenet: X-shape connection for camouflaged object detection
Mingchen Zhuge, Xiankai Lu, Yiyou Guo, Zhihua Cai, and Shuhan Chen · 2022
Later among the works it cites.
Advances in deep concealed scene understanding
Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, and Luc Van Gool · 2023
Closest in time.
High-resolution iterative feedback network for camouflaged object detection
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Exploring depth contribution for camouflaged object detection
Mochu Xiang, Jing Zhang, Yunqiu Lv, Aixuan Li, Yiran Zhong, and Yuchao Dai · 2021
Cited alongside, same era.
Uncertainty-guided transformer reasoning for camouflaged object detection
Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, and Deng-Ping Fan · 2021
Cited alongside, same era.
Mutual graph learning for camouflaged object detection
Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, and Deng-Ping Fan · 2021
Cited alongside, same era.
Inferring camouflaged objects by texture-aware interactive guidance network
Jinchao Zhu, Xiaoyu Zhang, Shuo Zhang, and Junnan Liu · 2021
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Cited alongside, same era.
Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Donghao Luo, Ying Tai, and Ling Shao · 2023
Closest in time.
Feature shrinkage pyramid for camouflaged object detection with transformers
Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, and Huan Xiong · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Towards deeper understanding of camouflaged object detection
Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Nick Barnes, and Deng-Ping Fan · 2023
Closest in time.
Distraction-aware camouflaged object segmentation
H Mei, X Yang, Y Zhou, GP Ji, X Wei, and DP Fan · 2023
Closest in time.
Source-free depth for object pop-out
Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, and Luc Van Gool · 2023
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Go closer to see better: Camouflaged object detection via object area amplification and figure-ground conversion
Haozhe Xing, Yan Wang, Xujun Wei, Hao Tang, Shuyong Gao, and Wenqiang Zhang · 2023
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Multimodal learning with transformers: A survey
Peng Xu, Xiatian Zhu, and David A Clifton · 2023
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mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration
Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou · 2023
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Camoformer: Masked separable attention for camouflaged object detection
Bowen Yin, Xuying Zhang, Qibin Hou, Bo-Yuan Sun, Deng-Ping Fan, and Luc Van Gool · 2023
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Referring camouflaged object detection
Xuying Zhang, Bowen Yin, Zheng Lin, Qibin Hou, Deng-Ping Fan, and Ming-Ming Cheng · 2023
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Mffn: Multi-view feature fusion network for camouflaged object detection
Dehua Zheng, Xiaochen Zheng, Laurence T Yang, Yuan Gao, Chenlu Zhu, and Yiheng Ruan · 2023
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