EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval
<|reference_start|>EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval: 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.<|reference_end|>
@article{hummel2024egocvr:,
    title={EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video
  Retrieval},
    author={Thomas Hummel, Shyamgopal Karthik, Mariana-Iuliana Georgescu, Zeynep
  Akata},
    journal={arXiv preprint arXiv:2407.16658},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16658},
    primaryClass={cs.CV}
}