Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos (2410.02763v1)

Abstract: There has been growing sentiment recently that modern large multimodal models (LMMs) have addressed most of the key challenges related to short video comprehension. As a result, both academia and industry are gradually shifting their attention towards the more complex challenges posed by understanding long-form videos. However, is this really the case? Our studies indicate that LMMs still lack many fundamental reasoning capabilities even when dealing with short videos. We introduce Vinoground, a temporal counterfactual LMM evaluation benchmark encompassing 1000 short and natural video-caption pairs. We demonstrate that existing LMMs severely struggle to distinguish temporal differences between different actions and object transformations. For example, the best model GPT-4o only obtains ~50% on our text and video scores, showing a large gap compared to the human baseline of ~90%. All open-source multimodal models and CLIP-based models perform much worse, producing mostly random chance performance. Through this work, we shed light onto the fact that temporal reasoning in short videos is a problem yet to be fully solved. The dataset and evaluation code are available at https://vinoground.github.io.

An Examination of the Vinoground Evaluation Benchmark for Temporal Reasoning in Large Multimodal Models

The paper presents "Vinoground," a novel benchmark developed to scrutinize the temporal reasoning capabilities of Large Multimodal Models (LMMs) when tasked with interpreting short videos. Contrary to the growing consensus that LMMs have achieved significant progress in comprehending short video content, the authors argue that extant models often exhibit severe deficiencies, particularly in dense temporal situations. This claim is substantiated by empirical evidence showing underperformance in state-of-the-art models—both proprietary and open-source—across various metrics.

The dataset, Vinoground, consists of 1000 short, natural video-caption pairs engineered to challenge the models by requiring them to distinguish subtle temporal differences between video events and transformations. The benchmark draws inspiration from Winoground, a benchmark focusing on visio-linguistic compositional reasoning in images, and extends its domain to video by incorporating temporal counterfactuals. The evaluation framework leverages text, video, and group scores to assess the model's ability to match appropriate captions to videos, thereby comprehensively evaluating textual, visual, and temporal reasoning capabilities.

Vinoground is categorized into three major categories—object, action, and viewpoint—and four minor categories—interaction, cyclical, spatial, and contextual—to enable a nuanced evaluation of the models’ performances. This structured approach helps in isolating specific capabilities and weaknesses of LMMs in different temporal scenarios. For text-generative models, GPT-4o outperforms others under specific configurations, particularly when employing Chain-of-Thought (CoT) prompting, although its overall performance on temporal reasoning remains insufficient compared to human annotation efforts conducted in the paper. The analysis of models operating with varying numbers of frames highlights a critical insight: more frames generally benefit the models; however, an overwhelming quantity of frames can degrade performance, indicating limitations in current models' capabilities regarding temporal signal isolation.

A profound implication of this research is the clear indication that LMMs are yet to achieve human-level temporal reasoning even in the context of short video sequences. This gap reflects the current models' tendency to exhibit 'single-frame bias,' reducing the inherently dynamic nature of video analysis to a static image comprehension task. Thus, Vinoground's primary contribution lies in exposing this deficiency, thereby serving as a crucial tool in the ongoing development and evolution of LMMs.

On a theoretical level, this paper challenges the assumption that advances in LMMs automatically translate to robust temporal reasoning, urging a more focused development trajectory in AI research. Practically, it underscores the necessity for enhancing models' capabilities to process and understand dense temporal information—an indispensable skill for applications in real-time decision-making, autonomous navigation, and other domains where temporal understanding is key.

In conclusion, the insights derived from Vinoground unveil the considerable work needed to elevate LMMs to fully grasp and interpret the temporal nuances of video content. Future endeavors in this space should aim to integrate temporality not merely as an auxiliary enhancement to static image-processing capabilities but as a foundational component in the pursuit of more intelligent multimodal systems.