Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding
Minerva-Ego is a new benchmark for evaluating complex egocentric visual reasoning, featuring multi-step multimodal questions and densely annotated reasoning traces. Experiments show a large gap between state-of-the-art models and human performance, with significant improvements when providing 'where' and 'when' hints.
[2605.15342] Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding
[Submitted on 14 May 2026]
Title:Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding
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Abstract: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 intermediate reasoning steps, and most provide answers only in the text domain. We introduce Minerva-Ego, a benchmark 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 spatiotemporal mask annotations. Through extensive evaluations, we identify that prompting frontier models with hints of 'where' and 'when' to look yields substantial improvements in performance. Minerva-Ego can be downloaded at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2605.15342 [cs.CV]
(or arXiv:2605.15342v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.15342
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sudheendra Vijayanarasimhan [view email] [v1] Thu, 14 May 2026 19:12:20 UTC (5,140 KB)
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