Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

• URL: http://arxiv.org/abs/2302.14485v1
• Date: Tue, 28 Feb 2023 10:58:01 GMT
• Title: Memory-aided Contrastive Consensus Learning for Co-salient Object Detection
• Authors: Peng Zheng, Jie Qin, Shuo Wang, Tian-Zhu Xiang, Huan Xiong
• Abstract summary: Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. We propose a novel Memory-aided Contrastive Consensus Learning framework, which is capable of effectively detecting co-salient objects in real time. Experiments on all the latest CoSOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge models.
• Score: 30.92094260367798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for finding common objects. To achieve accurate CoSOD results with high-quality maps and high efficiency, we propose a novel Memory-aided Contrastive Consensus Learning (MCCL) framework, which is capable of effectively detecting co-salient objects in real time (~110 fps). To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories. Finally, to improve the quality and integrity of the predicted maps, we develop an Adversarial Integrity Learning (AIL) strategy to make the segmented regions more likely composed of complete objects with less surrounding noise. Extensive experiments on all the latest CoSOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge models, achieving the new state of the art (~5.9% and ~6.2% improvement in S-measure on CoSOD3k and CoSal2015, respectively). Our source codes, saliency maps, and online demos are publicly available at https://github.com/ZhengPeng7/MCCL.

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