DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes

• URL: http://arxiv.org/abs/2505.23179v1
• Date: Thu, 29 May 2025 07:16:16 GMT
• Title: DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes
• Authors: Sungjune Park, Hyunjun Kim, Junho Kim, Seongho Kim, Yong Man Ro,
• Abstract summary: Deep Inspection and Perception with RL (DIP-R1) is designed to enhance the visual perception capabilities of MLLMs.<n>DIP-R1 guides MLLMs through detailed inspection of visual scene via three simply designed rule-based reward modelings.<n>It achieves consistent and significant improvement across various in-domain and out-of-domain scenarios.
• Score: 51.895756593200296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant visual understanding capabilities, yet their fine-grained visual perception in complex real-world scenarios, such as densely crowded public areas, remains limited. Inspired by the recent success of reinforcement learning (RL) in both LLMs and MLLMs, in this paper, we explore how RL can enhance visual perception ability of MLLMs. Then we develop a novel RL-based framework, Deep Inspection and Perception with RL (DIP-R1) designed to enhance the visual perception capabilities of MLLMs, by comprehending complex scenes and looking through visual instances closely. DIP-R1 guides MLLMs through detailed inspection of visual scene via three simply designed rule-based reward modelings. First, we adopt a standard reasoning reward encouraging the model to include three step-by-step processes: 1) reasoning for understanding visual scenes, 2) observing for looking through interested but ambiguous regions, and 3) decision-making for predicting answer. Second, a variance-guided looking reward is designed to examine uncertain regions for the second observing process. It explicitly enables the model to inspect ambiguous areas, improving its ability to mitigate perceptual uncertainties. Third, we model a weighted precision-recall accuracy reward enhancing accurate decision-making. We explore its effectiveness across diverse fine-grained object detection data consisting of challenging real-world environments, such as densely crowded scenes. Built upon existing MLLMs, DIP-R1 achieves consistent and significant improvement across various in-domain and out-of-domain scenarios. It also outperforms various existing baseline models and supervised fine-tuning methods. Our findings highlight the substantial potential of integrating RL into MLLMs for enhancing capabilities in complex real-world perception tasks.

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