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arXiv Computer Vision By Shahaf Wagner, Gabriele Serussi, Dan Ben Ami, Tomer Galanti, Chaim Baskin

VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval

Read the original on arXiv Computer Vision →

VEDJE is a new video-text retrieval method that compresses video features into a reusable cache while keeping them distinct for each frame. It introduces feature-change prediction as an auxiliary training signal, enabling improved retrieval from the compressed cache without extra cost at query time. Experiments on MSR-VTT, MSVD, DiDeMo, and ActivityNet show that VEDJE boosts R@1 in both retrieval directions, achieving 59.8% text-to-video R@1 on MSR-VTT with a fine-tuned VideoCLIP-XL first stage and maintaining recall when the per-video cache is reduced to 12 KiB.

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