Wolf: Dense Video Captioning with a World Summarization Framework (2407.18908v2)

Abstract: We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging complementary strengths of Vision LLMs (VLMs). By utilizing both image and video models, our framework captures different levels of information and summarizes them efficiently. Our approach can be applied to enhance video understanding, auto-labeling, and captioning. To evaluate caption quality, we introduce CapScore, an LLM-based metric to assess the similarity and quality of generated captions compared to the ground truth captions. We further build four human-annotated datasets in three domains: autonomous driving, general scenes, and robotics, to facilitate comprehensive comparisons. We show that Wolf achieves superior captioning performance compared to state-of-the-art approaches from the research community (VILA1.5, CogAgent) and commercial solutions (Gemini-Pro-1.5, GPT-4V). For instance, in comparison with GPT-4V, Wolf improves CapScore both quality-wise by 55.6% and similarity-wise by 77.4% on challenging driving videos. Finally, we establish a benchmark for video captioning and introduce a leaderboard, aiming to accelerate advancements in video understanding, captioning, and data alignment. Webpage: https://wolfv0.github.io/.

• The paper introduces Wolf, a comprehensive video captioning framework that employs a mixture-of-experts strategy with chain-of-thought summarization to enhance temporal understanding.
• It develops CapScore, an LLM-based metric that assesses caption similarity and quality, effectively reducing hallucinations in generated video descriptions.
• Extensive benchmarking across diverse datasets demonstrates Wolf’s superior performance with significant improvements over state-of-the-art captioning models.

An Expert Review of "Wolf: Captioning Everything with a World Summarization Framework"

The research paper titled "Wolf: Captioning Everything with a World Summarization Framework" proposes Wolf, a novel framework designed to address the multifaceted challenges in video captioning through a mixture-of-experts approach. This paper makes substantial contributions to the field of video understanding by developing a sophisticated captioning model, a new evaluation metric, and comprehensive benchmark datasets.

Framework and Methodology

Wolf is built on the premise of leveraging multiple Vision LLMs (VLMs) to improve the quality and accuracy of video captions. The framework integrates both image-level and video-level models to capture and amalgamate complex information from visual data. Specifically, Wolf employs a "Chain-of-thought" summarization approach in image-level models, meticulously generating captions for sequential keyframes and later summarizing these to encapsulate temporal dynamics. This is further augmented by LLM-based (LLMs) summarization to enhance the coherence and depth of the generated video summaries.

The adoption of a mixture-of-experts strategy is fundamental to Wolf’s operation. It synergizes the diverse strengths of various models like CogAgent, GPT-4V, VILA-1.5, and Gemini-Pro-1.5. Each model contributes unique insights, which are collectively distilled to develop a comprehensive caption. This integration minimizes hallucinations—incorrect or fabricated information prevalent in singular model approaches—and ensures highly detailed, accurate reflections of video content.

CapScore: A Novel Evaluation Metric

In the absence of a standardized metric to assess the quality of video captions, the paper introduces CapScore, an LLM-based metric. CapScore evaluates two crucial aspects: caption similarity and caption quality. Caption similarity measures the alignment between generated and ground-truth captions, while caption quality assesses the informativeness and precision, emphasizing the reduction of hallucinations.

Benchmarking and Datasets

Wolf's efficacy is evaluated using four newly constructed datasets: Nuscenes interactive driving videos, Nuscenes normal driving videos, a general daily video set from Pexels, and robotics manipulation videos. These datasets facilitate a broad assessment of Wolf’s performance across different domains, from autonomous driving to general and robotics-specific scenes.

The paper also introduces the first standard evaluation benchmark for video captioning, coupled with a leaderboard to track and motivate advancements in the field. This is a strategic move to bring cohesion to the fragmented landscape of video understanding evaluations.

Experimental Results

Wolf demonstrates superior performance over existing state-of-the-art captioning models across all datasets. For instance, it shows significant improvements in CapScore metrics when compared to top-tier models like GPT-4V and Gemini-Pro-1.5. Notably, Wolf achieves a 55.6% enhancement in caption quality and a 77.4% improvement in similarity over challenging driving video datasets, illustrating its robustness in handling complex, dynamic video content.

Future Directions

The potential implications of Wolf are vast, both practically and theoretically. Practically, it can lead to enhanced video understanding capabilities that are critical in fields such as autonomous driving, where detailed and accurate video annotations are paramount for training and validation. Theoretically, the provision of a standard benchmarking dataset and evaluation metric (CapScore) lays foundational work for future research, fostering a unified approach to video captioning evaluations.

Potential future directions for Wolf include optimizing the computation efficiency of the framework, further refining CapScore for nuanced evaluation, and extending the framework to incorporate more diverse datasets and task-specific benchmarks.

Conclusion

Wolf sets a new standard in video captioning by integrating multiple expert models and introducing both a comprehensive evaluation metric and benchmark datasets. This framework shows considerable promise in advancing the state of video understanding research, making it an essential reference point for future work in the domain. The introduction of CapScore and the establishment of a public leaderboard further reinforce this paper’s contributions by providing essential tools for ongoing and future research in video captioning.