Papers › Video + CLIP Baseline for Ego4D Long-term Action Anticipation

Video + CLIP Baseline for Ego4D Long-term Action Anticipation

1 Jul 2022arXiv:2207.00579archive 2025-07-28

Srijan Das, Michael S. Ryoo

In this report, we introduce our adaptation of image-text models for long-term action anticipation. Our Video + CLIP framework makes use of a large-scale pre-trained paired image-text model: CLIP and a video encoder Slowfast network. The CLIP embedding provides fine-grained understanding of objects relevant for an action whereas the slowfast network is responsible for modeling temporal information within a video clip of few frames. We show that the features obtained from both encoders are complementary to each other, thus outperforming the baseline on Ego4D for the task of long-term action anticipation. Our code is available at github.com/srijandas07/clip_baseline_LTA_Ego4d.

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