Towards Video Thinking Test: A Holistic Benchmark for Advanced Video
  Reasoning and Understanding
Authors: name: Yuanhan Zhang, name: Yunice Chew, name: Yuhao Dong, name: Aria Leo, name: Bo Hu, name: Ziwei Liu

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.