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ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Wen-Da Jin · Jun Xu · Ming-Ming Cheng · Yi Zhang · Wei Guo

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #735

Intra-saliency and inter-saliency cues have been extensively studied for co-saliency detection (Co-SOD). Model-based methods produce coarse Co-SOD results due to hand-crafted intra- and inter-saliency features. Current data-driven models exploit inter-saliency cues, but undervalue the potential power of intra-saliency cues. In this paper, we propose an Intra-saliency Correlation Network (ICNet) to extract intra-saliency cues from the single image saliency maps (SISMs) predicted by any off-the-shelf SOD method, and obtain inter-saliency cues by correlation techniques. Specifically, we adopt normalized masked average pooling (NMAP) to extract latent intra-saliency categories from the SISMs and semantic features as intra cues. Then we employ a correlation fusion module (CFM) to obtain inter cues by exploiting correlations between the intra cues and single-image features. To improve Co-SOD performance, we propose a category-independent rearranged self-correlation feature (RSCF) strategy. Experiments on three benchmarks show that our ICNet outperforms previous state-of-the-art methods on Co-SOD. Ablation studies validate the effectiveness of our contributions. The PyTorch code is available at https://github.com/blanclist/ICNet.

Author Information

Wen-Da Jin (Tianjin University)
Jun Xu (Nankai University)
Ming-Ming Cheng (Nankai University)
Yi Zhang (Tianjin University)
Wei Guo (Tianjin University)

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