Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs
Haochen Wang, Yuhao Wang, Tao Zhang, Yikang Zhou, Yanwei Li, Jiacong Wang, Jiani Zheng, Ye Tian, Jiahao Meng, Zilong Huang, Guangcan Mai, Anran Wang, Yunhai Tong, Zhuochen Wang, Xiangtai Li, Zhaoxiang Zhang
TL;DR
This paper introduces GAR, a region-level multimodal model that integrates global context and prompt interactions for precise, compositional understanding of complex scenes, advancing visual reasoning and evaluation benchmarks.
Contribution
We propose GAR, a novel region-level MLLM with a RoI-aligned feature replay technique, enabling global context integration and multi-prompt interaction for enhanced visual understanding.
Findings
GAR achieves state-of-the-art captioning performance.
GAR outperforms existing models on multi-region reasoning benchmarks.
Zero-shot GAR-8B surpasses in-domain video understanding models.
Abstract
While Multimodal Large Language Models (MLLMs) excel at holistic understanding, they struggle in capturing the dense world with complex scenes, requiring fine-grained analysis of intricate details and object inter-relationships. Region-level MLLMs have been a promising step. However, previous attempts are generally optimized to understand given regions in isolation, neglecting crucial global contexts. To address this, we introduce Grasp Any Region (GAR) for comprehen- sive region-level visual understanding. Empowered by an effective RoI-aligned feature replay technique, GAR supports (1) precise perception by leveraging necessary global contexts, and (2) modeling interactions between multiple prompts. Together, it then naturally achieves (3) advanced compositional reasoning to answer specific free-form questions about any region, shifting the paradigm from passive description to active…
Peer Reviews
Decision·ICLR 2026 Poster
1. Proposes an innovative RoI-aligned feature replay technique that seamlessly integrates global context and local details, solving a key limitation of existing region-level MLLMs. 2. Develops GARBench, the first benchmark to systematically evaluate multi-prompt interaction and compositional reasoning, filling an evaluation gap. 3. Demonstrates strong generalization, including zero-shot transfer to video tasks, highlighting the model’s practical utility. Conducts comprehensive experiments (ablat
1. RoI-Align’s context binding validity could benefit from additional validation, as the paper does not include targeted experiments for complex scenes where misbinding irrelevant context might occur. Additionally, the ablations in Table 8 do not test whether shielding irrelevant global regions impacts the accuracy of extracted local features. 2. The Grasp Any Region-2.5M dataset and GARBench do not provide clear annotations or statistics on which tasks specifically depend on global context. Thi
- Understanding fine-grained details and object inter-relationships is critical for real-world applications of VLMs. This paper provides important contributions in training dataset, evaluation benchmark and model architechture. - The paper is clearly written and easy to read.
- Understanding the dense world is an important capability. However, the proposed GAR task represents only a specific task formulation within this direction. In particular, the paper constrains the use of masks as indicators of objects, which may introduce bias when evaluating a model’s true understanding of dense visual scenes. The compared models might simply lack the ability to interpret masks, rather than being genuinely deficient in understanding local details and relationships. - The propo
+ Clear motivation and reasonable architectural solution: GAR tackles the important problem to understand regional information in the vision-language model, and proposes the reasonable solution with a RoI-aligned feature replay strategy. This design allows global context retention while focusing on high-resolution local features. + Focus on inter-region relationships: Unlike prior works, which primarily handle single-object or localized descriptions, GAR explicitly models multi-prompt interacti
- Evaluation dependence on LLM judges: The authors rely on LLM-based evaluators (e.g., GPT-4) for qualitative assessment. This introduces potential bias due to stylistic or verbosity differences among models. Cross-validation with human ratings or standardized metrics would strengthen the claims. - Insufficient data transparency: The training pipeline involves multiple stages—seed captioner, LLM merger, and relational caption generation—but lacks detail on data deduplication and leakage checks.
Code & Models
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Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Explainable Artificial Intelligence (XAI)