← 研踪 PaperTrailCVF · CVPR 2026:cvf:c4a41c776c99f0341c5ebfd68c4b80e8 · 记录 #1
Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 04:15
摘要
Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation of the output (e.g. the answer to a question), without evaluation of inter- mediate reasoning steps, and most provide answers only in the text domain. We introduce Minerva-Ego, a bench- mark for evaluating complex egocentric visual reasoning. We extend recent high-quality video data sources recorded from egocentric / embodied settings with a set of challenging, multi-step multimodal questions and spatiotemporally-dense human-annotated reasoning traces. Benchmarking experiments show that state-of-the-art models still have a large gap to human performance. To investigate this gap in detail, we annotate each reasoning trace in the dataset with the objects of interest required to solve the question, as spatio-temporal mask annotations. Through extensive evaluations, we identify that prompting frontier models with hints of 'where' and when to look yields substantial improvements in performance.
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记录 #1 · 2026 · 最新观测版本
Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding
Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation of the output (e.g. the answer to a question), without evaluation of inter- mediate reasoning steps, and most provide answers only in the text domain. We introduce Minerva-Ego, a bench- mark for evaluating complex egocentric visual reasoning. We extend recent high-quality video data sources recorded from egocentric / embodied settings with a set of challenging, multi-step multimodal questions and spatiotemporally-dense human-annotated reasoning traces. Benchmarking experiments show that state-of-the-art models still have a large gap to human performance. To investigate this gap in detail, we annotate each reasoning trace in the dataset with the objects of interest required to solve the question, as spatio-temporal mask annotations. Through extensive evaluations, we identify that prompting frontier models with hints of 'where' and when to look yields substantial improvements in performance.
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