Minerva-Ego: 自己中心映像理解のための時空間ヒント
Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding
自己中心視点の映像理解における複雑な推論を評価するベンチマークを新たに構築し、最先端モデルが人間に及ばないことを示すとともに、時空間ヒントを与えることで性能が向上することを明らかにした。
著者: Arsha Nagrani, Jasper Uijilings, Shyamal Buch, Tobias Weyand, Sudheendra Vijayanarasimhan, Bo Hu, Ramin Mehran, David A Ross, Cordelia Schmid
分類: cs.CV, cs.LG
原文アブストラクト
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 https://github.com/google-deepmind/neptune.