LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval (2411.14505v1)

Abstract: Multimodal LLMs (MLLMs) are widely used for visual perception, understanding, and reasoning. However, long video processing and precise moment retrieval remain challenging due to LLMs' limited context size and coarse frame extraction. We propose the Large Language-and-Vision Assistant for Moment Retrieval (LLaVA-MR), which enables accurate moment retrieval and contextual grounding in videos using MLLMs. LLaVA-MR combines Dense Frame and Time Encoding (DFTE) for spatial-temporal feature extraction, Informative Frame Selection (IFS) for capturing brief visual and motion patterns, and Dynamic Token Compression (DTC) to manage LLM context limitations. Evaluations on benchmarks like Charades-STA and QVHighlights demonstrate that LLaVA-MR outperforms 11 state-of-the-art methods, achieving an improvement of 1.82% in [email protected] and 1.29% in [email protected] on the QVHighlights dataset. Our implementation will be open-sourced upon acceptance.

• The paper introduces LLaVA-MR, a novel framework that enhances video moment retrieval with advanced techniques like DFTE, IFS, and DTC.
• It improves temporal precision and reduces redundant processing, achieving notable accuracy gains on benchmarks like QVHighlights.
• The framework paves the way for applications in automated editing, surveillance, and multimodal research while inspiring future audio-visual integrations.

LLaVA-MR: Advancements in Multimodal Moment Retrieval

The paper "LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval" presents a sophisticated approach to tackle the prominent challenges in video moment retrieval using Multimodal LLMs (MLLMs). The authors adeptly situate their contributions within the landscape of existing methods, focusing on improving temporal awareness and precision in identifying brief yet significant moments within lengthy videos.

Core Contributions

LLaVA-MR is presented as an innovative framework that brings together various techniques designed to overcome the limitations of existing MLLMs. Key aspects of the methodology include:

1. Dense Frame and Time Encoding (DFTE): This method is designed to enhance temporal perceptual accuracy. DFTE focuses on capturing fine-grained spatial and temporal features, thereby improving the contextual grounding of video frames. By addressing both the spatial details and their temporal alignment, DFTE stands as an essential feature extractor within LLaVA-MR.
2. Informative Frame Selection (IFS): Tackling the redundancy issue in video analysis, IFS classifies frames into key and non-key categories. By identifying and preserving frames of significant visual and motion changes, this module ensures valuable content is retained while discarding lesser information, thereby enhancing the effectiveness of data processing.
3. Dynamic Token Compression (DTC): This method compresses the sequence length without losing crucial information, efficiently managing the context size limitations inherent in LLMs. The compression results in performance improvements both in processing speed and in localization accuracy.

Experimental Validation and Results

The authors conducted evaluations on established benchmarks such as Charades-STA and QVHighlights and demonstrated that LLaVA-MR outperforms numerous state-of-the-art methods. Notably, LLaVA-MR achieved a considerable 1.82% improvement in [email protected] and a 1.29% increment in [email protected] on QVHighlights, marking its substantial efficacy over competing models.

Discussion on Model Implications

From a theoretical standpoint, the paper enriches the understanding of how multimodal data can be effectively utilized for video analysis tasks. The innovative application of dense frame operations coupled with time encoding offers pathways for future research into context-rich moment analysis. Practically, the LLaVA-MR framework is poised to impact sectors requiring precise video content analysis, such as automated video editing, security surveillance, and media archiving.

Speculations on Future Developments

The trajectory of LLaVA-MR opens new research avenues, especially with regard to integrating additional modalities like audio into the analysis pipeline to further enrich the contextual understanding of video segments. Moreover, enhancing the model's interpretability through methods like generating confidence scores for video clips or integrating techniques such as Chain-of-Thought reasoning could refine its applicability and usefulness in real-world scenarios.

In conclusion, LLaVA-MR marks a significant step forward in leveraging the capabilities of MLLMs for complex video moment retrieval tasks. By addressing both data efficiency and temporal precision, this paper lays the groundwork for future explorations in the domain of multimodal machine learning.