Visual Memory and Textual Alignment for Cross-Modal Few-Shot Segmentation
To address this challenge, we propose ViTA-SAM, a framework built upon SAM 3 that integrates visual memory and textual features through cross-modal alignment.
ProofPaper ↗
Key points
- Few-shot segmentation (FSS) aims to segment target objects with limited annotated examples.
- In particular, SAM 3 provides strong visual representations and supports text-guided segmentation.
- To bridge the gap between semantic and visual representations, we introduce a cross-modal alignment module that conditions textual features on memory-enhanced visual representations, yielding more consistent and visually grounded predictions.
- In addition, we propose a pseudo-label refinement strategy that updates the memory with predicted query masks, enabling query-adaptive representation learning.
Sources (1)
- [1]Visual Memory and Textual Alignment for Cross-Modal Few-Shot SegmentationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:35 AM
To address this challenge, we propose ViTA-SAM, a framework built upon SAM 3 that integrates visual memory and textual features through cross-modal alignment.
Few-shot segmentation (FSS) aims to segment target objects with limited annotated examples.
Extractive summary: sentences quoted from the sources.
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