DIVE: Deep-search Iterative Video Exploration A Technical Report for the CVRR Challenge at CVPR 2025
DIVE employs iterative reasoning with semantic decomposition, achieving 81.44% accuracy on CVRR-ES, winning the challenge.
Key Findings
Methodology
DIVE integrates a six-step process: first, intent estimation interprets the question’s core purpose; second, questions are decomposed into sub-questions based on semantic cues; third, an agent uses GPT-4.1 and Grounding DINO for detailed analysis of key segments and objects; fourth, sub-questions are refined iteratively based on responses; fifth, a continuation judgment determines whether further reasoning is needed; sixth, answers are synthesized into a final response. Video summaries leverage object detection to describe key objects and their transitions, enhancing understanding. This multi-round approach significantly improves accuracy in complex video QA tasks.
Key Results
- On CVRR-ES, DIVE achieved 81.44% accuracy on the test set, outperforming previous best of 75.03%. Validation results showed 91.55%. Ablation studies confirmed that question decomposition increased accuracy by 4.34%, intent estimation added 1.75%, and object-centric summarization contributed 0.42%. The combined modules led to superior robustness and performance.
- Compared to existing methods, DIVE demonstrated exceptional ability in handling complex scenarios, especially in identifying subtle object interactions and scene changes. The iterative reasoning process proved crucial for deep understanding, outperforming single-pass models.
- Ablation experiments revealed that each module—question breakdown, intent estimation, and object detection—individually contributed to performance gains. The multi-step reasoning framework proved effective in diverse and challenging video contexts, validating its design for real-world applications.
Significance
This work advances video question answering by overcoming the limitations of single-step models, enabling deeper semantic understanding and more accurate responses in complex scenes. The integration of intent estimation and object-centric summaries addresses key challenges in long video comprehension, paving the way for more intelligent multimodal systems. Its success demonstrates the potential for multi-round reasoning frameworks to revolutionize video understanding, with broad implications for AI applications in surveillance, content analysis, and human-computer interaction.
Technical Contribution
The paper introduces a novel multi-round iterative reasoning framework combining semantic question decomposition, intent estimation, and object-based video summarization. It leverages GPT-4.1 and Grounding DINO for detailed analysis, enabling fine-grained understanding of complex scenes. The multi-cycle inference mechanism enhances robustness and interpretability, setting a new state-of-the-art in video QA. The approach also innovatively integrates object detection into the reasoning pipeline, offering a new engineering paradigm for multimodal AI.
Novelty
This is the first work to combine multi-step iterative reasoning with object-centric video summarization for complex video question answering. Unlike previous end-to-end models, DIVE decomposes questions into sub-queries, iteratively refines answers, and explicitly models object transitions, leading to significant performance improvements. Its modular design and multi-cycle inference set a new benchmark for robustness and accuracy in the domain.
Limitations
- The approach requires substantial computational resources due to multiple inference cycles and object detection, limiting real-time deployment. It also depends heavily on the quality of object detection and language models, which may falter in ambiguous or cluttered scenes. Additionally, the system's performance in unseen or highly dynamic environments remains to be validated, indicating room for further robustness improvements.
Future Work
Future efforts will focus on optimizing inference efficiency, possibly through dynamic module selection or model pruning. Exploring reinforcement learning to adaptively control reasoning steps could improve speed. Extending the framework to larger, more diverse datasets and real-world scenarios will test its generalizability. Moreover, integrating more advanced multimodal fusion techniques may further enhance understanding of complex scenes.
AI Executive Summary
Video understanding remains a fundamental challenge in AI, especially when dealing with complex, real-world scenes that require multi-step reasoning. Traditional models often struggle with long videos and intricate semantic relationships, leading to limited accuracy and interpretability. The CVRR-ES benchmark has set a new standard by emphasizing robustness and detailed reasoning, pushing researchers to develop more sophisticated solutions.
In response, this work introduces DIVE, a novel framework that combines semantic decomposition, intent estimation, and iterative inference to address these challenges. The system first interprets the question’s core purpose by analyzing a video summary, then decomposes it into manageable sub-questions tailored to specific objects or scene aspects. Using GPT-4.1 and Grounding DINO, DIVE performs detailed analysis on key frames and object regions, refining questions based on responses and deciding whether further reasoning is necessary. This loop continues until a comprehensive understanding is achieved, culminating in an accurate, context-aware answer.
Experimental results on CVRR-ES demonstrate the effectiveness of DIVE, with a test accuracy of 81.44%, surpassing all competing methods. Ablation studies confirm that each module—question breakdown, intent estimation, object detection—contributes significantly to overall performance. The approach’s modularity and multi-cycle reasoning enable it to handle complex queries with high robustness, marking a significant step forward in video question answering.
Looking ahead, efforts will aim to improve computational efficiency, extend applicability to broader datasets, and incorporate adaptive reasoning strategies. This research not only advances the state-of-the-art in video understanding but also opens new avenues for multimodal AI systems capable of nuanced, multi-step reasoning in dynamic environments.
Deep Dive
Plain Language Accessible to non-experts
想象你在看一本非常复杂的故事书，每一章都充满了细节和人物关系。要理解整个故事，你不能只看一页，而是要逐步拆开每个章节，理解每个人物的动机和事件的因果关系。DIVE就像是一个聪明的读者，它会先猜测故事的主要线索，然后逐步拆解每个细节，反复确认，直到完全理解整个故事。它会用放大镜观察每个角色的动作，用笔记整理每个事件的关系，最后告诉你整个故事的真相。这种逐步分析和反复确认的方法，让它能在复杂的故事中找到答案，就像在复杂的视频中找到隐藏的细节一样。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏。每次你都不知道下一块拼图在哪里，也不知道每块拼图怎么拼在一起。于是，你先观察整个拼图，猜测哪个部分可能藏着关键线索，然后把拼图拆开，逐块拼好。每拼完一块，你会再检查一下，确认它是不是正确的，必要时还会拆掉重新拼。这个过程反复进行，直到拼出完整的图像。DIVE就像这个拼图高手，它会不断拆解问题，逐步拼凑答案，确保每个细节都拼对了。这样，即使面对非常复杂的视频，它也能找到正确的答案，就像拼出一幅完整的画一样。
Abstract
In this report, we present the winning solution that achieved the 1st place in the Complex Video Reasoning & Robustness Evaluation Challenge 2025. This challenge evaluates the ability to generate accurate natural language answers to questions about diverse, real-world video clips. It uses the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES) benchmark, which consists of 214 unique videos and 2,400 question-answer pairs spanning 11 categories. Our method, DIVE (Deep-search Iterative Video Exploration), adopts an iterative reasoning approach, in which each input question is semantically decomposed and solved through stepwise reasoning and progressive inference. This enables our system to provide highly accurate and contextually appropriate answers to even the most complex queries. Applied to the CVRR-ES benchmark, our approach achieves 81.44% accuracy on the test set, securing the top position among all participants. This report details our methodology and provides a comprehensive analysis of the experimental results, demonstrating the effectiveness of our iterative reasoning framework in achieving robust video question answering. The code is available at https://github.com/PanasonicConnect/DIVE