ViSTa Dataset: Do vision-language models understand sequential tasks?

• URL: http://arxiv.org/abs/2411.13211v2
• Date: Thu, 21 Nov 2024 16:37:32 GMT
• Title: ViSTa Dataset: Do vision-language models understand sequential tasks?
• Authors: Evžen Wybitul, Evan Ryan Gunter, Mikhail Seleznyov, David Lindner,
• Abstract summary: Using vision-language models (VLMs) as reward models in reinforcement learning holds promise for reducing costs and improving safety. We introduce ViSTa, a dataset for evaluating Vision-based understanding of Sequential Tasks. ViSTa comprises over 4,000 videos with step-by-step descriptions in virtual home, Minecraft, and real-world environments.
• Score: 6.039062076849557
• License:
• Abstract: Using vision-language models (VLMs) as reward models in reinforcement learning holds promise for reducing costs and improving safety. So far, VLM reward models have only been used for goal-oriented tasks, where the agent must reach a particular final outcome. We explore VLMs' potential to supervise tasks that cannot be scored by the final state alone. To this end, we introduce ViSTa, a dataset for evaluating Vision-based understanding of Sequential Tasks. ViSTa comprises over 4,000 videos with step-by-step descriptions in virtual home, Minecraft, and real-world environments. Its novel hierarchical structure -- basic single-step tasks composed into more and more complex sequential tasks -- allows a fine-grained understanding of how well VLMs can judge tasks with varying complexity. To illustrate this, we use ViSTa to evaluate state-of-the-art VLMs, including CLIP, ViCLIP, and GPT-4o. We find that, while they are all good at object recognition, they fail to understand sequential tasks, with only GPT-4o achieving non-trivial performance.

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