Revisit to Segment: Working Memory Distillation for Reasoning Segmentation
Multimodal large language models (MLLMs) have approached image segmentation by reasoning about visual content and predicting target locations. Their generated responses contain reasoning traces and localization proposals that can serve as working memory when revisiting the same image and query. Our exploration reveals that MLLMs benefit from using this self-generated working memory as context, leading to enhanced reasoning segmentation. Motivated by this finding, we seek to strengthen the backbone model's reasoning segmentation capabilities by distilling the guidance gained from revisiting prior attempts, enabling it to benefit with or without working memory at inference time. To this end...
| Authors | Cilin Yan, Yilun Qiu, Wanyang Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 September 2026 |
| Last updated at source | 3 October 2026 |
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