Multi-Content Interaction Network for Few-Shot Segmentation
This work improves few-shot segmentation for computer vision applications, representing an incremental advance by enhancing existing methods with multi-scale interactions.
The paper tackles the problem of few-shot segmentation by addressing the shift issue between different layers and scales in support-query pairs, proposing a Multi-Content Interaction Network (MCINet) that achieves state-of-the-art performance on benchmarks like COCO.
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.