← 研踪 PaperTrailarXiv:2510.16785 · v2
Robust Reasoning Segmentation for Open-World MLLMs without Catastrophic Forgetting
首次提交 2025-10-19 18:21
版本更新 2026-11-11 08:13
雷达观测 2026-11-13 11:11
摘要
Ensuring trustworthiness and versatility in Open-World Visual Recognition is a core goal of Multimodal Large Language Models (MLLMs). While integrating segmentation is desirable, prevailing methods require invasive fine-tuning, which often leads to catastrophic forgetting of the model's general-purpose capabilities. This trade-off between task-specific performance and general reliability fundamentally undermines the goal of building unified, trustworthy systems. We introduce LENS (Leveraging kEypoiNts for MLLMs' Segmentation), a novel plug-and-play paradigm designed to equip MLLMs with pixel-level ability without compromising their foundational integrity. LENS attaches a lightweight head to a completely frozen MLLM, interpreting the intrinsic spatial cues embedded in attention maps. It extracts keypoints and translates them into prompt-compatible features for a mask decoder. Extensive experiments validate that LENS achieves segmentation performance competitive with retraining-based methods. Crucially, it largely preserves the MLLM's open-world generalization, avoiding the degradation observed in fine-tuned counterparts. By decoupling segmentation from generation, LENS offers a robust and efficient path toward truly multi-talented and reliable vision models.
作者备注：Published in International Journal of Computer Vision (IJCV), 2026
观测到的版本
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v2 · 2026-11-11 08:13 · 最新观测版本
Robust Reasoning Segmentation for Open-World MLLMs without Catastrophic Forgetting
Ensuring trustworthiness and versatility in Open-World Visual Recognition is a core goal of Multimodal Large Language Models (MLLMs). While integrating segmentation is desirable, prevailing methods require invasive fine-tuning, which often leads to catastrophic forgetting of the model's general-purpose capabilities. This trade-off between task-specific performance and general reliability fundamentally undermines the goal of building unified, trustworthy systems. We introduce LENS (Leveraging kEypoiNts for MLLMs' Segmentation), a novel plug-and-play paradigm designed to equip MLLMs with pixel-level ability without compromising their foundational integrity. LENS attaches a lightweight head to a completely frozen MLLM, interpreting the intrinsic spatial cues embedded in attention maps. It extracts keypoints and translates them into prompt-compatible features for a mask decoder. Extensive experiments validate that LENS achieves segmentation performance competitive with retraining-based methods. Crucially, it largely preserves the MLLM's open-world generalization, avoiding the degradation observed in fine-tuned counterparts. By decoupling segmentation from generation, LENS offers a robust and efficient path toward truly multi-talented and reliable vision models.
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