Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding
This addresses the need for better benchmarks to assess video reasoning in AI, though it is incremental as it builds on existing evaluation frameworks.
The authors tackled the problem of evaluating video large language models' ability to interpret real-world videos by introducing the Video Thinking Test (Video-TT), a benchmark with 1,000 YouTube Shorts videos and adversarial questions, revealing a significant performance gap between models and humans.
Human intelligence requires correctness and robustness, with the former being foundational for the latter. In video understanding, correctness ensures the accurate interpretation of visual content, and robustness maintains consistent performance in challenging conditions. Despite advances in video large language models (video LLMs), existing benchmarks inadequately reflect the gap between these models and human intelligence in maintaining correctness and robustness in video interpretation. We introduce the Video Thinking Test (Video-TT), to assess if video LLMs can interpret real-world videos as effectively as humans. Video-TT reflects genuine gaps in understanding complex visual narratives, and evaluates robustness against natural adversarial questions. Video-TT comprises 1,000 YouTube Shorts videos, each with one open-ended question and four adversarial questions that probe visual and narrative complexity. Our evaluation shows a significant gap between video LLMs and human performance.