Abstract

Precisely naming the action depicted in a video can be a challenging and oftentimes ambiguous task. In contrast to object instances represented as nouns (e.g. dog, cat, chair, etc.), in the case of actions, human annotators typically lack a consensus as to what constitutes a specific action (e.g. jogging versus running). In practice, a given video can contain multiple valid positive annotations for the same action. As a result, video datasets often contain significant levels of label noise and overlap between the atomic action classes. In this work, we address the challenge of training multi-label action recognition models from only single positive training labels. We propose two approaches that are based on generating pseudo training examples sampled from similar instances within the train set. Unlike other approaches that use model-derived pseudo-labels, our pseudo-labels come from human annotations and are selected based on feature similarity. To validate our approaches, we create a new evaluation benchmark by manually annotating a subset of EPIC-Kitchens-100's validation set with multiple verb labels. We present results on this new test set along with additional results on a new version of HMDB-51, called Confusing-HMDB-102, where we outperform existing methods in both cases.
Data and code are available at https://github.com/kiyoon/verb_ambiguity
Original languageEnglish
Title of host publicationProceedings of The 33rd British Machine Vision Conference (BMVC 2022)
PublisherBMVA Press
Number of pages18
Publication statusPublished - 25 Nov 2022
EventThe 33rd British Machine Vision Conference, 2022 - London, United Kingdom
Duration: 21 Nov 202224 Nov 2022
Conference number: 33
https://www.bmvc2022.org/

Conference

ConferenceThe 33rd British Machine Vision Conference, 2022
Abbreviated titleBMVC 2022
Country/TerritoryUnited Kingdom
CityLondon
Period21/11/2224/11/22
Internet address

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