MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation

• URL: http://arxiv.org/abs/2503.13881v1
• Date: Tue, 18 Mar 2025 04:23:09 GMT
• Title: MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation
• Authors: Donggon Jang, Yucheol Cho, Suin Lee, Taehyeon Kim, Dae-Shik Kim,
• Abstract summary: We present a dataset called Multi-target and Multi-granularity Reasoning (MMR)<n>MMR comprises 194K complex and implicit instructions that consider multi-target, object-level, and part-level aspects.<n>We propose a straightforward yet effective framework for multi-target, object-level, and part-level reasoning segmentation.
• Score: 14.144097766150397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fusion of Large Language Models with vision models is pioneering new possibilities in user-interactive vision-language tasks. A notable application is reasoning segmentation, where models generate pixel-level segmentation masks by comprehending implicit meanings in human instructions. However, seamless human-AI interaction demands more than just object-level recognition; it requires understanding both objects and the functions of their detailed parts, particularly in multi-target scenarios. For example, when instructing a robot to \textit{turn on the TV"}, there could be various ways to accomplish this command. Recognizing multiple objects capable of turning on the TV, such as the TV itself or a remote control (multi-target), provides more flexible options and aids in finding the optimized scenario. Furthermore, understanding specific parts of these objects, like the TV's button or the remote's button (part-level), is important for completing the action. Unfortunately, current reasoning segmentation datasets predominantly focus on a single target object-level reasoning, which limits the detailed recognition of an object's parts in multi-target contexts. To address this gap, we construct a large-scale dataset called Multi-target and Multi-granularity Reasoning (MMR). MMR comprises 194K complex and implicit instructions that consider multi-target, object-level, and part-level aspects, based on pre-existing image-mask sets. This dataset supports diverse and context-aware interactions by hierarchically providing object and part information. Moreover, we propose a straightforward yet effective framework for multi-target, object-level, and part-level reasoning segmentation. Experimental results on MMR show that the proposed method can reason effectively in multi-target and multi-granularity scenarios, while the existing reasoning segmentation model still has room for improvement.

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