ROVER: Routing Object-Centric Visual Evidence for Grounded Multi-Image Reasoning
ROVER enhances multi-image reasoning accuracy via object-centric visual evidence routing.
Key Findings
Methodology
ROVER is a lightweight plugin for efficient global visual evidence routing in multi-image reasoning. It injects step-specific token triplets to aggregate reasoning context, distill intra-image cues, and integrate history-aware evidence across objects and images within a visual working space.
Key Results
- ROVER improved answer accuracy by 4.8% and grounding accuracy by 14.6% on the MM-GCoT dataset.
- On the VideoEspresso dataset, answer accuracy increased by 8.6%.
- The model demonstrated strong transferability, outperforming the base model by 4.7% on average across diverse benchmarks.
Significance
ROVER addresses efficiency issues in existing methods for multi-image reasoning, especially in scenarios requiring global scene understanding and inter-object relation modeling. It provides new approaches for complex visual reasoning tasks in multimodal large language models.
Technical Contribution
ROVER achieves global visual evidence routing through object-centric differential attention and visual working space, offering a more efficient decoding mechanism compared to existing RoI reencoding and resampling methods.
Novelty
ROVER is the first to implement object-centric visual evidence routing, overcoming limitations of traditional RoI methods and providing comprehensive scene understanding.
Limitations
- ROVER may face computational cost issues when processing high-resolution images.
- Further optimization is needed for more complex scenarios.
Future Work
Future work could explore ROVER's application in more multimodal tasks and optimize its computational efficiency.
AI Executive Summary
Multimodal large language models have made significant advances in visual reasoning, but existing methods face efficiency and accuracy challenges in multi-image reasoning. ROVER offers a new solution through object-centric visual evidence routing. It injects token triplets to aggregate reasoning context, distill intra-image cues, and integrate historical evidence, thereby improving reasoning accuracy. Experimental results show ROVER's excellent performance on MM-GCoT and VideoEspresso datasets, significantly enhancing answer and grounding accuracy. This research is not only significant in academia but also provides new visual reasoning methods for the industry. Although ROVER still has limitations in handling complex scenarios, its innovation and efficiency offer important directions for future research.
Deep Analysis
Background
Multimodal large language models have made significant advances in visual and language understanding. Researchers have attempted to enhance models' visual reasoning capabilities through language-centric reasoning mechanisms. However, existing methods face efficiency and accuracy challenges in multi-image reasoning, especially in scenarios requiring global scene understanding and inter-object relation modeling.
Core Problem
Existing RoI-based methods are inefficient in multi-image reasoning, struggling to achieve global scene understanding and inter-object relation modeling. This leads to errors and hallucinations in complex scenarios.
Innovation
ROVER offers a new solution through object-centric visual evidence routing. It injects token triplets to aggregate reasoning context, distill intra-image cues, and integrate historical evidence, thereby improving reasoning accuracy.
Methodology
- �� ROVER uses object-centric differential attention to distill intra-image cues.
- �� Visual working space integrates history-aware evidence across objects and images.
- �� Token triplets aggregate reasoning context.
Experiments
Experiments were conducted using MM-GCoT and VideoEspresso datasets, strictly adhering to original datasets and evaluation protocols. ROVER demonstrated superior performance in answer and grounding accuracy compared to existing methods.
Results
ROVER improved answer accuracy by 4.8% and grounding accuracy by 14.6% on the MM-GCoT dataset. On the VideoEspresso dataset, answer accuracy increased by 8.6%. The model demonstrated strong transferability, outperforming the base model by 4.7% on average across diverse benchmarks.
Applications
ROVER can be applied to multimodal tasks requiring global scene understanding and inter-object relation modeling, such as autonomous driving and intelligent surveillance.
Limitations & Outlook
ROVER may face computational cost issues when processing high-resolution images. Further optimization is needed for more complex scenarios.
Plain Language Accessible to non-experts
Imagine a factory where workers need to collaborate across different production lines to complete a product. Existing methods focus only on one production line, ignoring the coordination of the entire factory. ROVER acts like a coordinator, ensuring information from each production line is integrated, ultimately producing a higher quality product.
ELI14 Explained like you're 14
Imagine playing a multiplayer game where you need to cooperate with teammates to win. Existing methods focus only on yourself, ignoring teammates' actions. ROVER acts like a team commander, ensuring information from each teammate is integrated, helping the team achieve victory.
Glossary
ROVER (Routing Object-centric Visual Evidence)
A lightweight plugin for multi-image reasoning that enhances efficiency through object-centric visual evidence routing.
ROVER injects token triplets upon each object grounding prediction.
MM-GCoT (Multimodal Chain-of-Thought)
A dataset for evaluating multimodal reasoning capabilities, including complex visual and language tasks.
ROVER demonstrated excellent performance on MM-GCoT.
VideoEspresso
A dataset for evaluating video reasoning capabilities, including multi-image reasoning tasks.
ROVER improved answer accuracy on VideoEspresso.
Object-centric Differential Attention (DiffAttn)
An attention mechanism for extracting intra-image cues while suppressing distractors.
ROVER uses object-centric differential attention to distill cues.
Visual Working Space (VWS)
A structured routing substrate for integrating history-aware evidence across objects and images.
ROVER integrates evidence through the visual working space.
Open Questions Unanswered questions from this research
- 1 How to further optimize ROVER for more complex scenarios?
- 2 How to address computational cost issues of ROVER on high-resolution images?
Applications
Immediate Applications
Autonomous Driving
ROVER can be used in autonomous driving systems to enhance vehicle understanding of complex scenes.
Long-term Vision
Intelligent Surveillance
ROVER can be used in intelligent surveillance systems for more efficient scene analysis and event recognition.
Abstract
Multimodal Large Language Models (MLLMs) have increasingly localized and interleaved visual evidence for deliberative reasoning. Grounding-based approaches typically focus on regions of interest (RoIs) by injecting cropped image patches or RoI-specific features into the reasoning context. However, such designs can weaken holistic scene understanding and inter-object relations, while incurring decoding costs that scale with the number and size of RoIs. Alternatively, adaptive visual feature selection often requires fine-grained supervision or complex heuristics. To address these limitations, we propose ROVER (Routing Object-centric Visual Evidence for grounded multi-image Reasoning), a lightweight, learnable plugin for efficient global visual evidence routing. Upon each object grounding prediction, ROVER injects a step-specific token triplet to synergistically: (i) aggregate the ongoing reasoning context, (ii) distill intra-image cues into a visual working space via object-centric differential attention, and (iii) route and integrate history-aware evidence across objects and images within this space for subsequent reasoning. We integrate ROVER into Qwen2.5-VL-7B and develop an interleaved SFT-to-GRPO training pipeline. Strictly adhering to the original datasets and evaluation protocols, our method achieves the best performance on MM-GCoT (+4.8% answer accuracy, +14.6% grounding accuracy) and VideoEspresso (+8.6% answer accuracy). The VideoEspresso-trained model demonstrates strong transferability, outperforming the base model by +4.7% on average across diverse benchmarks.