UnLoc: A Unified Framework for Video Localization Tasks
Shen Yan Xuehan Xiong Arsha Nagrani Anurag Arnab Zhonghao Wang Weina Ge David Ross Cordelia Schmid
While large-scale image-text pretrained models such as CLIP have been used
for multiple video-level tasks on trimmed videos, their use for temporal
localization in untrimmed videos is still a relatively unexplored task. We
design a new approach for this called UnLoc, which uses pretrained image and
text towers, and feeds tokens to a video-text fusion model. The output of the
fusion module are then used to construct a feature pyramid in which each level
connects to a head to predict a per-frame relevancy score and start/end time
displacements. Unlike previous works, our architecture enables Moment
Retrieval, Temporal Localization, and Action Segmentation with a single stage
model, without the need for action proposals, motion based pretrained features
or representation masking. Unlike specialized models, we achieve state of the
art results on all three different localization tasks with a unified approach.
Code will be available at: \url{https://github.com/google-research/scenic}.