  In Composed Video Retrieval, a video and a textual description which modifies
the video content are provided as inputs to the model. The aim is to retrieve
the relevant video with the modified content from a database of videos. In this
challenging task, the first step is to acquire large-scale training datasets
and collect high-quality benchmarks for evaluation. In this work, we introduce
EgoCVR, a new evaluation benchmark for fine-grained Composed Video Retrieval
using large-scale egocentric video datasets. EgoCVR consists of 2,295 queries
that specifically focus on high-quality temporal video understanding. We find
that existing Composed Video Retrieval frameworks do not achieve the necessary
high-quality temporal video understanding for this task. To address this
shortcoming, we adapt a simple training-free method, propose a generic
re-ranking framework for Composed Video Retrieval, and demonstrate that this
achieves strong results on EgoCVR. Our code and benchmark are freely available
at https://github.com/ExplainableML/EgoCVR.
