Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2410.03290v1)

Abstract: Video LLMs (Video-LLMs) have demonstrated remarkable capabilities in coarse-grained video understanding, however, they struggle with fine-grained temporal grounding. In this paper, we introduce Grounded-VideoLLM, a novel Video-LLM adept at perceiving and reasoning over specific video moments in a fine-grained manner. We identify that current Video-LLMs have limitations for fine-grained video understanding since they lack effective temporal modeling and timestamp representation. In light of this, we sharpen our model by incorporating (1) an additional temporal stream to encode the relationships between frames and (2) discrete temporal tokens enriched with specific time knowledge to represent timestamps. To optimize the training of Grounded-VideoLLM, we employ a multi-stage training scheme, beginning with simple video-captioning tasks and progressively introducing video temporal grounding tasks of increasing complexity. To further enhance Grounded-VideoLLM's temporal reasoning capability, we also curate a grounded VideoQA dataset by an automatic annotation pipeline. Extensive experiments demonstrate that Grounded-VideoLLM not only excels in fine-grained grounding tasks such as temporal sentence grounding, dense video captioning, and grounded VideoQA, but also shows great potential as a versatile video assistant for general video understanding.

Analyzing Grounded-VideoLLM: Advancing Fine-grained Temporal Grounding in Video LLMs

The paper "Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video LLMs" presents a Video LLM (Video-LLM) specifically designed for enhanced temporal grounding in fine-grained video analysis. While existing Video-LLMs demonstrate capabilities in coarse video understanding, they typically falter in accurately perceiving and reasoning over specific video moments. This paper addresses such limitations by introducing a structured approach through two core mechanisms: a two-stream encoding architecture and a novel representation of timestamps using discrete temporal tokens.

Model Architecture and Core Innovations

Grounded-VideoLLM integrates a two-stream approach to efficiently encode both spatial and temporal components. The spatial stream utilizes a pre-trained image encoder to extract keyframe representations, capturing vital appearance details. Simultaneously, the temporal stream engages a robust video encoder to analyze motion dynamics from dense frame sequences, focusing on segment-wise temporal encoding. This dual approach complements spatial insights with temporal continuity, thereby constructing comprehensive video representations.

The introduction of discrete temporal tokens signifies a pivotal advancement, addressing inefficiencies associated with traditional timestamp text representations—inefficiencies linked to the inherent design of LLMs, which are not optimized for processing numerical data. This paper strategically extends the LLM vocabulary to include these temporal tokens, fostering seamless transitions between temporal positions and textual outputs.

Progressive Training Methodology

Grounded-VideoLLM's efficacy derives partly from a progressive training strategy. This approach begins with video-caption alignment to establish foundational model knowledge, and subsequently transitions to temporal token alignment to refine the model’s ability to integrate timestamps with semantic content. The final stage involves multi-task instruction tuning, incorporating diverse datasets designed to enhance both temporal reasoning capabilities and instruction responsiveness. Notably, a grounded VideoQA dataset is curated to further substantiate the model's temporal reasoning proficiency.

Empirical Evaluation and Results

Through extensive empirical evaluations, Grounded-VideoLLM exhibits superior performance across several fine-grained temporal grounding tasks, such as temporal sentence grounding, dense video captioning, and grounded VideoQA. Its performance metrics indicate a robust capacity for detecting and articulating specific events within videos, outperforming existing Video-LLMs. Moreover, despite its focus on temporal tasks, Grounded-VideoLLM maintains strong performance in general video understanding benchmarks.

Theoretical and Practical Implications

The research demonstrates significant theoretical implications, primarily in enhancing temporal comprehension within Video-LLMs. By effectively combining spatial and temporal insights, the model sets a precedent for further exploration of temporal grounding mechanisms. Practically, the application extends to areas requiring precise video content analysis, including video surveillance, content creation, and media indexing, highlighting the potential impact on industries reliant on video data interpretation.

Future Directions

Speculation on future developments suggests that potential exists for refining temporal tokens and exploring more sophisticated multi-modal interactions within radically larger datasets. Additionally, integrating unsupervised learning paradigms or exploring domain-specific applications could enhance the adaptability and effectiveness of such models.

In summary, the Grounded-VideoLLM advances the field of Video-LLMs by offering refined mechanisms for temporal grounding, thereby augmenting both the analytical precision and the application scope of video understanding systems.