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EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval

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arxiv 2407.16658 v1 pith:3TI3F3BH submitted 2024-07-23 cs.CV

Thomas Hummel , Shyamgopal Karthik , Mariana-Iuliana Georgescu , Zeynep Akata This is my paper · ORCID
classification cs.CV
keywords videocomposedegocvrretrievalbenchmarkhigh-qualitycontentdatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for Training-Free Zero-Shot Composed Image Retrieval

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A one-stage, training-free method using GPT-4o with reflective chain-of-thought prompting sets new state-of-the-art benchmark numbers for composed image retrieval, though code and a full baseline comparison are not ye...

  2. Do Language Models Understand Time?

    cs.CV 2024-12 conditional novelty 3.0 of 10

    A survey arguing that video-LLMs rely on pretrained encoders and short-biased datasets, leaving them weak at long-term temporal reasoning such as causality and event progression.

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