Multi-Content Interaction Network for Few-Shot Segmentation
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Abstract (arXiv)
Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies. Most existing approaches focus on extracting high-level representations of the same layers for support-query correlations, neglecting the shift issue between different layers and scales, due to the huge difference between support and query samples. In this paper, we propose a Multi-Content Interaction Network (MCINet) to remedy this issue by fully exploiting and interacting with the multi-scale contextual information contained in the support-query pairs to supplement the same-layer correlations. Specifically, MCINet improves FSS from the perspectives of boosting the query representations by incorporating the low-level structural information from another query branch into the high-level semantic features, enhancing the support-query correlations by exploiting both the same-layer and adjacent-layer features, and refining the predicted results by a multi-scale mask prediction strategy, with which the different scale contents have bidirectionally interacted. Experiments on two benchmarks demonstrate that our approach reaches SOTA performances and outperforms the best competitors with many desirable advantages, especially on the challenging COCO dataset.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 50 of its 65 references that are in the map. First posted on arXiv 11 Mar 2023.
Builds on
50 of 65 references.
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38 more
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.