Task-specific Meta-feature Selection for Few-shot Segmentation
Keywords: Computer Vision, Semantic Segmentation, Few-Shot Segmentation
TL;DR: We propose a task-specific gradient-based few-shot segmentation algorithm that can serve as a plug-and-play module embedded in multiple few-shot segmentation methods, consistently enhancing performance.
Abstract: Few-shot segmentation (FSS) aims to segment new category images given only a few labeled samples. Most previous works concentrate on the design of intricate query decoders to perform feature matching or aggregation between the support and query. In this paper, we revisit a widely overlooked aspect of existing FSS methods, i.e., the exploration of pretrained backbone features. We find that treating all feature channels equally is suboptimal and propose a Task-specific Channel-wise Modulation Network (TCMNet) to focus more attention on task-aware channels, facilitating more effective utilization of pre-trained features. The proposed TCMNet enjoys several merits. First, we design a self-modulation block that injects the gradient information into channel-wise attention layers, thereby enhancing the discriminability between target and background features. Second, a cross-calibration block is introduced to align the support features toward the query according to the target gradient and representations, which mitigates the impact of intra-class diversity. Extensive experimental results on COCO-20i and Pascal-5i benchmarks demonstrate that the TCMNet, as a general plugin, consistently achieves significant improvements over different query decoders and also achieves state-of-the-art results. In addition, the decent performance achieved by exploring the backbone features may inspire another direction for developing more comprehensive FSS models.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 5807
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