  The goal in episodic memory (EM) is to search a long egocentric video to
answer a natural language query (e.g., "where did I leave my purse?"). Existing
EM methods exhaustively extract expensive fixed-length clip features to look
everywhere in the video for the answer, which is infeasible for long
wearable-camera videos that span hours or even days. We propose SpotEM, an
approach to achieve efficiency for a given EM method while maintaining good
accuracy. SpotEM consists of three key ideas: 1) a novel clip selector that
learns to identify promising video regions to search conditioned on the
language query; 2) a set of low-cost semantic indexing features that capture
the context of rooms, objects, and interactions that suggest where to look; and
3) distillation losses that address the optimization issues arising from
end-to-end joint training of the clip selector and EM model. Our experiments on
200+ hours of video from the Ego4D EM Natural Language Queries benchmark and
three different EM models demonstrate the effectiveness of our approach:
computing only 10% - 25% of the clip features, we preserve 84% - 97% of the
original EM model's accuracy. Project page:
https://vision.cs.utexas.edu/projects/spotem
