OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding? (2501.05510v2)

Abstract: Temporal Awareness, the ability to reason dynamically based on the timestamp when a question is raised, is the key distinction between offline and online video LLMs. Unlike offline models, which rely on complete videos for static, post hoc analysis, online models process video streams incrementally and dynamically adapt their responses based on the timestamp at which the question is posed. Despite its significance, temporal awareness has not been adequately evaluated in existing benchmarks. To fill this gap, we present OVO-Bench (Online-VideO-Benchmark), a novel video benchmark that emphasizes the importance of timestamps for advanced online video understanding capability benchmarking. OVO-Bench evaluates the ability of video LLMs to reason and respond to events occurring at specific timestamps under three distinct scenarios: (1) Backward tracing: trace back to past events to answer the question. (2) Real-time understanding: understand and respond to events as they unfold at the current timestamp. (3) Forward active responding: delay the response until sufficient future information becomes available to answer the question accurately. OVO-Bench comprises 12 tasks, featuring 644 unique videos and approximately human-curated 2,800 fine-grained meta-annotations with precise timestamps. We combine automated generation pipelines with human curation. With these high-quality samples, we further developed an evaluation pipeline to systematically query video LLMs along the video timeline. Evaluations of nine Video-LLMs reveal that, despite advancements on traditional benchmarks, current models struggle with online video understanding, showing a significant gap compared to human agents. We hope OVO-Bench will drive progress in video LLMs and inspire future research in online video reasoning. Our benchmark and code can be accessed at https://github.com/JoeLeelyf/OVO-Bench.

• The paper introduces OVO-Bench, a benchmark that evaluates Video-LLMs' temporal reasoning through backward tracing, real-time perception, and forward active responding.
• The evaluation reveals a significant performance gap between human understanding and current models, especially in processing dynamic, real-world video sequences.
• The study highlights opportunities for future research to develop enhanced temporal and causal reasoning capabilities for robust online video comprehension.

An Analysis of OVO-Bench for Video-LLMs

The paper presents OVO-Bench, a novel benchmark designed for evaluating the capabilities of video-language learning models (Video-LLMs) in understanding streaming online video content. This work addresses the critical distinction between offline and online video understanding models, particularly focusing on temporal awareness—a pivotal component for real-time video comprehension.

Key Contributions

The primary contribution of the paper lies in establishing OVO-Bench, an online video benchmark designed to evaluate Video-LLMs' temporal reasoning abilities across three defined scenarios: Backward Tracing, Real-Time Visual Perception, and Forward Active Responding.

1. Backward Tracing: This involves tracing back past events in videos to provide contextually accurate responses. This aspect evaluates the model's ability to recall and interpret past data efficiently to answer queries based on a chronological sequence of events.
2. Real-Time Visual Perception: The model must interpret and respond to visual information as it unfolds in real-time. This requires the model to process inputs continuously and adapt to newly available data promptly, all while maintaining accuracy.
3. Forward Active Responding: This task evaluates the model's ability to withhold a response until sufficient future information is available, thus simulating a proactive thinking process commonly referred to as the 'Video Chain-of-Time' reasoning.

OVO-Bench is comprehensive and includes 12 tasks with 644 videos, covering diverse domains and human-curated annotations. These tasks simulate realistic video understanding challenges that a real-world AI assistant would need to address.

Evaluation and Findings

The evaluations reveal that while existing Video-LLMs demonstrate strong capabilities on traditional benchmarks, they face significant challenges in online video understanding, particularly concerning temporal reasoning and continuous adaptation. Some notable observations from evaluations include:

• Performance Gap: There is a marked disparity between human performance and current Video-LLMs in processing and interpreting real-world video inputs through OVO-Bench.
• Complex Tasks: Current models underperform in scenarios involving complex event sequences and real-time processing, highlighting the need for improved temporal and causal reasoning capabilities in future models.
• Advancement Opportunities: The findings underscore an opportunity for future research to focus on narrowing the gap between the demonstrated capabilities of Video-LLMs under controlled settings compared to complex, real-world scenarios.

Implications and Future Directions

Practically, this benchmark is poised to guide the development of more sophisticated AI systems capable of operating in dynamic environments. Theoretically, it provides a framework for evaluating the intersection of vision and language understanding in temporally complex settings, pushing the boundaries of what current AI systems can achieve.

Looking ahead, advancements may include further integration of memory-based systems to enhance context retention and temporal reasoning, potentially moving towards models that approximate human-level video comprehension capabilities. Additionally, cultivating models that can dynamically adapt to real-time inputs will be crucial for applications in autonomous systems, smart assistance, and interactive AI.

In conclusion, OVO-Bench sets a precedent for rigorous evaluation of temporal awareness in video understanding, offering insights into both the limitations and potential of Video-LLMs in real-world applications. This initiative may inspire further innovations aimed at closing the gap between human-level understanding and machine-based video analysis.