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Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
Mandela Patrick · Dylan Campbell · Yuki Asano · Ishan Misra · Florian Metze · Christoph Feichtenhofer · Andrea Vedaldi · João Henriques

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @ None #None
In video transformers, the time dimension is often treated in the same way as the two spatial dimensions. However, in a scene where objects or the camera may move, a physical point imaged at one location in frame $t$ may be entirely unrelated to what is found at that location in frame $t+k$. These temporal correspondences should be modeled to facilitate learning about dynamic scenes. To this end, we propose a new drop-in block for video transformers - trajectory attention - that aggregates information along implicitly determined motion paths. We additionally propose a new method to address the quadratic dependence of computation and memory on the input size, which is particularly important for high resolution or long videos. While these ideas are useful in a range of settings, we apply them to the specific task of video action recognition with a transformer model and obtain state-of-the-art results on the Kinetics, Something-Something V2, and Epic-Kitchens datasets.

Author Information

Mandela Patrick (University of Oxford)
Dylan Campbell (University of Oxford)
Yuki Asano (University of Amsterdam)
Ishan Misra (Facebook AI Research)
Florian Metze (Carnegie Mellon University)
Christoph Feichtenhofer (Facebook AI Research)
Andrea Vedaldi (University of Oxford / Facebook AI Research)
João Henriques (University of Oxford)

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