MCAF: Efficient Agent-based Video Understanding Framework through Multimodal Coarse-to-Fine Attention Focusing

Even in the era of rapid advances in large models, video understanding, particularly long videos, remains highly challenging. Compared with textual or image-based information, videos commonly contain more information with redundancy, requiring large models to strategically allocate attention at a global level for accurate comprehension. To address this, we propose MCAF, an agent-based, training-free framework perform video understanding through Multimodal Coarse-to-fine Attention Focusing. The key innovation lies in its ability to sense and prioritize segments of the video that are highly relevant to the understanding task. First, MCAF hierarchically concentrates on highly relevant frames through multimodal information, enhancing the correlation between the acquired contextual information and the query. Second, it employs a dilated temporal expansion mechanism to mitigate the risk of missing crucial details when extracting information from these concentrated frames. In addition, our framework incorporates a self-reflection mechanism utilizing the confidence level of the model's responses as feedback. By iteratively applying these two creative focusing strategies, it adaptively adjusts attention to capture highly query-connected context and thus improves response accuracy. MCAF outperforms comparable state-of-the-art methods on average. On the EgoSchema dataset, it achieves a remarkable 5% performance gain over the leading approach. Meanwhile, on Next-QA and IntentQA datasets, it outperforms the current state-of-the-art standard by 0.2% and 0.3% respectively. On the Video-MME dataset, which features videos averaging nearly an hour in length, MCAF also outperforms other agent-based methods.

DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering

Retrieval-Augmented Generation (RAG) has significantly demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks, such as Question-Answering (QA). RAG expands the query context by incorporating external knowledge bases to enhance the response accuracy. However, it would be inefficient to access LLMs multiple times for each query and unreliable to retrieve all the relevant documents by a single query. We find that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. To mine the relevance, a two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers while maintaining efficiency. Also, a small classifier is applied to two different selection strategies to determine the contribution of the retrieved documents to answering the query and retrieve the relatively relevant documents. Meanwhile, DR-RAG call the LLMs only once, which significantly improves the efficiency of the experiment. The experimental results on multi-hop QA datasets show that DR-RAG can significantly improve the accuracy of the answers and achieve new progress in QA systems.