Papers › Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach
Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach
Mir Rayat Imtiaz Hossain, Mennatullah Siam, Leonid Sigal, James J. Little
The emergence of attention-based transformer models has led to their extensive use in various tasks, due to their superior generalization and transfer properties. Recent research has demonstrated that such models, when prompted appropriately, are excellent for few-shot inference. However, such techniques are under-explored for dense prediction tasks like semantic segmentation. In this work, we examine the effectiveness of prompting a transformer-decoder with learned visual prompts for the generalized few-shot segmentation (GFSS) task. Our goal is to achieve strong performance not only on novel categories with limited examples, but also to retain performance on base categories. We propose an approach to learn visual prompts with limited examples. These learned visual prompts are used to prompt a multiscale transformer decoder to facilitate accurate dense predictions. Additionally, we introduce a unidirectional causal attention mechanism between the novel prompts, learned with limited examples, and the base prompts, learned with abundant data. This mechanism enriches the novel prompts without deteriorating the base class performance. Overall, this form of prompting helps us achieve state-of-the-art performance for GFSS on two different benchmark datasets: COCO-20ⁱ and Pascal-5ⁱ, without the need for test-time optimization (or transduction). Furthermore, test-time optimization leveraging unlabelled test data can be used to improve the prompts, which we refer to as transductive prompt tuning.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Generalized Few-Shot Semantic Segmentation | COCO-20i (1-shot) | VisualPromptGFSS | Mean Base and Novel | 36.05 | #1 of 6 | Archive leaderboard | report |
| Generalized Few-Shot Semantic Segmentation | COCO-20i (5-shot) | VisualPromptGFSS | Mean Base and Novel | 42.48 | #1 of 5 | Archive leaderboard | report |
| Generalized Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | VisualPromptGFSS | Mean Base and Novel | 58.11 | #1 of 5 | Archive leaderboard | report |
| Generalized Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | VisualPromptGFSS | Mean Base and Novel | 66.27 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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