Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or
  True Temporal Understanding?
Existing video understanding benchmarks often conflate knowledge-based and
purely image-based questions, rather than clearly isolating a model's temporal
reasoning ability, which is the key aspect that distinguishes video
understanding from other modalities. We identify two major limitations that
obscure whether higher scores truly indicate stronger understanding of the
dynamic content in videos: (1) strong language priors, where models can answer
questions without watching the video; and (2) shuffling invariance, where
models maintain similar performance on certain questions even when video frames
are temporally shuffled. To alleviate these issues, we propose VBenchComp, an
automated pipeline that categorizes questions into different domains:
LLM-Answerable, Semantic, and Temporal. Specifically, LLM-Answerable questions
can be answered without viewing the video; Semantic questions remain answerable
even when the video frames are shuffled; and Temporal questions require
understanding the correct temporal order of frames. The rest of the questions
are labeled as Others. This can enable fine-grained evaluation of different
capabilities of a video LLM. Our analysis reveals nuanced model weaknesses that
are hidden by traditional overall scores, and we offer insights and
recommendations for designing future benchmarks that more accurately assess
video LLMs.
68396605ac00da416d094bbf
Bo Feng
68396605ac00da416d094bc0
Zhengfeng Lai
68396605ac00da416d094bc1
Shiyu Li
68396605ac00da416d094bc2
Zizhen Wang
68396605ac00da416d094bc3
Simon Wang
68396605ac00da416d094bc4
Ping Huang
68396605ac00da416d094bc5
Meng Cao
Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or
  True Temporal Understanding?
Existing video understanding benchmarks often conflate knowledge-based and
purely image-based questions, rather than clearly isolating a model's temporal
reasoning ability, which is the key aspect that distinguishes video
understanding from other modalities. We identify two major limitations that
obscure whether higher scores truly indicate stronger understanding of the
dynamic content in videos: (1) strong language priors, where models can answer
questions without watching the video; and (2) shuffling invariance, where
models maintain similar performance on certain questions even when video frames
are temporally shuffled. To alleviate these issues, we propose VBenchComp, an
automated pipeline that categorizes questions into different domains:
LLM-Answerable, Semantic, and Temporal. Specifically, LLM-Answerable questions
can be answered without viewing the video; Semantic questions remain answerable
even when the video frames are shuffled; and Temporal questions require
understanding the correct temporal order of frames. The rest of the questions
are labeled as Others. This can enable fine-grained evaluation of different
capabilities of a video LLM. Our analysis reveals nuanced model weaknesses that
are hidden by traditional overall scores, and we offer insights and
recommendations for designing future benchmarks that more accurately assess
video LLMs.
VBenchComp, an automated pipeline, categorizes video LLM questions into different domains to evaluate temporal reasoning and isolate model weaknesses beyond overall scores.
video understanding LLM-Answerable Semantic Temporal