Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering
Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth references. This paradigm suffers from the “one-to-many” nature of video description, where high-quality captions are often penalized for lexical mismatches or valid shifts in visual focus. Furthermore, such assessments are typically...
What happened
Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth references. This paradigm suffers from the “one-to-many” nature of video description, where high-quality captions are often penalized for lexical mismatches or valid shifts in visual focus. Furthermore, such assessments are typically...
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1 reports · 1 original report · 1 independent
- Apple Machine Learning ResearchPrimary source · Supports · EN · 100%Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering ↗
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- Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering Observed
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