How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs
(2405.03690)Abstract
Recent advancements in LLMs have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding tasks. These models have the potential to be deployed in real-world applications such as robotics, AI assistants, medical surgery, and autonomous vehicles. The widespread adoption of Video-LMMs in our daily lives underscores the importance of ensuring and evaluating their robust performance in mirroring human-like reasoning and interaction capabilities in complex, real-world contexts. However, existing benchmarks for Video-LMMs primarily focus on general video comprehension abilities and neglect assessing their reasoning capabilities over complex videos in the real-world context, and robustness of these models through the lens of user prompts as text queries. In this paper, we present the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES), a novel benchmark that comprehensively assesses the performance of Video-LMMs across 11 diverse real-world video dimensions. We evaluate 9 recent models, including both open-source and closed-source variants, and find that most of the Video-LMMs, especially open-source ones, struggle with robustness and reasoning when dealing with complex videos. Based on our analysis, we develop a training-free Dual-Step Contextual Prompting (DSCP) technique to enhance the performance of existing Video-LMMs. Our findings provide valuable insights for building the next generation of human-centric AI systems with advanced robustness and reasoning capabilities. Our dataset and code are publicly available at: https://mbzuai-oryx.github.io/CVRR-Evaluation-Suite/.
Overview
-
Video Large Multi-modal Models (Video-LMMs) integrate vision and language capabilities to understand complex video content and textual descriptions, and are vital in fields like robotics and autonomous vehicles.
-
The Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES) is introduced as a benchmark designed to test Video-LMMs across real-world scenarios, emphasizing reasoning abilities and robust responses, which previous benchmarks lacked.
-
The paper highlights the introduction of Dual-Step Contextual Prompting (DSCP) to improve Video-LMMs' reasoning and robustness, and discusses the future implications of improved AI models in sensitive and everyday environments.
Evaluating the Complexity and Robustness of Video Large Multi-modal Models
Introduction to Video-LMMs and Their Challenges
Video Large Multi-modal Models (Video-LMMs) are pushing the boundaries of AI by integrating vision with language capabilities, aiming to perform tasks that require understanding both video content and textual descriptions. They are increasingly influential in fields such as robotics, surveillance, and autonomous vehicles. It's essential, however, to challenge these models with tasks that demand a deep understanding of complex video content, contextual nuances, and robust responses to text queries. Existing benchmarks have often fallen short in demanding reasoning beyond simple video comprehension.
The CVRR-ES Benchmark: A Solution to Old Problems
The Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES) emerges as a novel benchmark designed to thoroughly evaluate Video-LMMs across diverse and complex real-world video dimensions. It aims to address the gaps left by prior benchmarks which focused on basic comprehension rather than reasoning abilities or real-world applicability. The CVRR-ES consists of 11 video evaluation dimensions addressing different aspects of video understanding and interaction. These dimensions span scenarios from complex multi-action videos to understanding emotionally and socially rich contexts.
Evaluation Dimensions Overview:
- Multiple Actions in a Single Video: Tests if the model can identify and reason about various actions taken within the same snippet.
- Fine-Grained Action Understanding: Challenges the model's ability to discern subtly different actions.
- Partial Actions and Time Order Understanding: Examines if models can understand actions that are partially shown or need temporal context.
- Robustness to Confusing Queries: Includes evaluating responses to nonexistent actions or scenes, demanding high model precision and robust error handling.
- Context Sensitivity: Through unusual activities, emotional, and social contexts, models must infer the right information from complex, sometimes deceptive scenarios.
Key Findings from Testing Video-LMMs with CVRR-ES
Upon evaluating several contemporary Video-LMMs using the CVRR-ES, several crucial insights have come to light. Most notably, even advanced models struggled significantly across several challenging dimensions, particularly in handling partial actions and complex query scenarios. For instance, the state-of-the-art Video-LLaVA model only scored an average of 15.92% across various tasks, significantly lower than the human benchmark of over 95%.
Chain-of-thought prompting techniques, when repurposed from pure language models to Video-LMMs, showed potential but didn't fully bridge the gap in model performance. The CVRR-ES benchmarks also exposed a general over-affirmation bias in several models, where they tend to confirm the presence of actions or details not mentioned or contradicted by the video content.
Towards Future Improvements: Introducing Dual-Step Contextual Prompting (DSCP)
In response to these challenges, the research introduces a Dual-Step Contextual Prompting (DSCP) technique designed to enhance Video-LMMs' reasoning ability and robustness. This training-free approach focuses on refining the model's focus during inference by promoting more detailed video-specific reasoning and adaptability to complex queries.
The Two Steps of DSCP:
- Contextual Reasoning: Guides the model to deeply analyze the video content beyond surface-level details, prepping it to manage higher complexity in user queries.
- Robust Response Generation: Couples the detailed context with the user’s actual query to ensure responses are both accurate to the video content and robust to query variations.
What the Future Holds
The implications of the CVRR-ES and innovations like DSCP are vast for the deployment of AI in sensitive and everyday environments. By demanding and fostering advanced reasoning, context understanding, and robust interaction capabilities, we edge closer to deploying AI systems that truly understand and interact with the complexity of real-world environments. Future developments might see these benchmarks and techniques becoming standard tools in refining and evaluating AI systems across industries.
Create an account to read this summary for free: