Guided Attention for Next Active Object @ EGO4D STA Challenge
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Abstract (arXiv)
In this technical report, we describe the Guided-Attention mechanism based solution for the short-term anticipation (STA) challenge for the EGO4D challenge. It combines the object detections, and the spatiotemporal features extracted from video clips, enhancing the motion and contextual information, and further decoding the object-centric and motion-centric information to address the problem of STA in egocentric videos. For the challenge, we build our model on top of StillFast with Guided Attention applied on fast network. Our model obtains better performance on the validation set and also achieves state-of-the-art (SOTA) results on the challenge test set for EGO4D Short-Term Object Interaction Anticipation Challenge.
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References · 13
- 2017Attention Is All You Needcited by 60,361
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2018SlowFast Networks for Video Recognitioncited by 1,867
- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,556
- 2021Anticipative Video Transformercited by 200
- 2022MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognitioncited by 189
- 2019What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attentioncited by 105
- 2019Next-Active-Object prediction from Egocentric Videoscited by 55
- 2021Forecasting Action through Contact Representations from First Person Videocited by 46
- 2023StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipationcited by 23
- 2023Anticipating Next Active Objects for Egocentric Videoscited by 8
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