Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization
This work addresses the challenge of improving action localization accuracy for video analysis applications by integrating audio, though it is incremental as it builds on existing video-only methods.
The paper tackles the problem of temporal action localization in untrimmed videos by incorporating audio modality, which had been unexploited, and achieves new state-of-the-art performance with 54.34 mAP@0.5 on ActivityNet-1.3 and 57.18 mAP@0.5 on THUMOS14.
State of the art architectures for untrimmed video Temporal Action Localization (TAL) have only considered RGB and Flow modalities, leaving the information-rich audio modality totally unexploited. Audio fusion has been explored for the related but arguably easier problem of trimmed (clip-level) action recognition. However, TAL poses a unique set of challenges. In this paper, we propose simple but effective fusion-based approaches for TAL. To the best of our knowledge, our work is the first to jointly consider audio and video modalities for supervised TAL. We experimentally show that our schemes consistently improve performance for state of the art video-only TAL approaches. Specifically, they help achieve new state of the art performance on large-scale benchmark datasets - ActivityNet-1.3 (54.34 mAP@0.5) and THUMOS14 (57.18 mAP@0.5). Our experiments include ablations involving multiple fusion schemes, modality combinations and TAL architectures. Our code, models and associated data are available at https://github.com/skelemoa/tal-hmo.