Cosalpure: Learning Concept from Group Images for Robust Co-Saliency Detection
Jiayi Zhu, Qing Guo, Felix Juefei-Xu, Yihao Huang, Yang Liu, Geguang Pu
摘要
Concept Learning Concept-guided Purification Co-salient Object Detectors c T2I Diffusion Group Images Containing some Adv. Examples Purified Group Images Detection Results Detection Results CosalPure Validation of c Learned concept Figure 1 . Examples of our method COSALPURE and comparative results before and after purification. COSALPURE comprises two modules: group-image concept learning and concept-guided purification. Firstly, the concept learning module inputs a group of images that contain some adversarial cases and obtain their shared co-salient semantic information (i.e., the learned concept), denoted as c. We can validate the effectiveness of the learned c through the visualization via a text-to-image (T2I) diffusion model. Secondly, steered by the previously learned concept, we employ certain diffusion generation techniques to purify the entire group of images. Before our purification, the co-salient object detection results are poor, but after purification, the detection results are satisfactory. Please enlarge to see more details.
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它引用的顶会 Paper11
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- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- EGNet: Edge Guidance Network for Salient Object DetectionJiaxing Zhao, Jiang-Jiang Liu, Deng-Ping Fan, Yang Cao 等ICCV 2019 · 被引用 1,054 次
- Diffusion Models for Adversarial PurificationWeili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao 等ICML 2022 · 被引用 663 次
- What the DAAM: Interpreting Stable Diffusion Using Cross AttentionRaphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang 等ACL 2023 · 被引用 93 次
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