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[bibtex]@InProceedings{Zhong_2023_WACV, author = {Zhong, Zeyun and Schneider, David and Voit, Michael and Stiefelhagen, Rainer and Beyerer, J\"urgen}, title = {Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6068-6077} }
Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation
Abstract
Although human action anticipation is a task which is inherently multi-modal, state-of-the-art methods on well known action anticipation datasets leverage this data by applying ensemble methods and averaging scores of uni-modal anticipation networks. In this work we introduce transformer based modality fusion techniques, which unify multi-modal data at an early stage. Our Anticipative Feature Fusion Transformer (AFFT) proves to be superior to popular score fusion approaches and presents state-of-the-art results outperforming previous methods on EpicKitchens-100 and EGTEA Gaze+. Our model is easily extensible and allows for adding new modalities without architectural changes. Consequently, we extracted audio features on EpicKitchens-100 which we add to the set of commonly used features in the community.
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