An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition

Kiyoon Kim, Davide Moltisanti, Oisin Mac Aodha, Laura Sevilla-Lara

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

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.
Original languageEnglish
Title of host publication33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022
PublisherBMVA Press
Number of pages14
Publication statusPublished - 10 Oct 2022

Bibliographical note

Acknowledgements: This work was in part supported by the Turing 2.0 ‘Enabling Advanced Autonomy’ project funded by the EPSRC and the Alan Turing Institute.

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