Video-Text Retrieval

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8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval
Oct 8, 2026Shahaf Wagner, Gabriele Serussi, Dan Ben Ami +2Video-Text Retrieval Video Retrieval
INSIGHT Lab, Ben-Gurion University of the Negev, Israel · Decart AI · Texas A&M University, College Station
MARS: What Retrieval Signals Are Hidden in Multimodal Large Language Models for Text-Video Retrieval?
Sep 2, 2026Uicheol Jung, Juyoung Hong, Geuntaek Lim +1Video-Text Retrieval Multi-Vector Retrieval
Sejong University, Seoul, Republic of Korea
TAME: Temporal-Aware Mixture-of-Experts for Text-Video Retrieval
Sep 2, 2026Uicheol Jung, Juyoung Hong, Hojung Kwon +1Video-Text Retrieval Contrastive Language-Image Pretraining
Sejong University, Seoul, Republic of Korea · Wisenut, Seongnam, Republic of Korea
USV: Towards Understanding the User-generated Short-form Videos
May 20, 2026Haoyue Cheng, Su Xu, Liwei Jin +3Video-Text Retrieval Vision-Language Retrieval
State Key Laboratory for Novel Software Technology, Nanjing University, China · SenseTime Research
Interactive Multi-Turn Retrieval for Health Videos
May 2, 2026Chengzheng Wu, Ke Qiu, Baoming Zhang +3Healthcare Video-Text Retrieval
Wuxi Dipont School of Arts and Science · Malou Tech · Chongqing University +2
EagleNet: Energy-Aware Fine-Grained Relationship Learning Network for Text-Video Retrieval
Mar 26, 2026Yuhan Chen, Pengwen Dai, Chuan Wang +2Video-Text Retrieval Vision-Language Retrieval
Sun Yat-sen University, Guangzhou, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China · Shenzhen Key Laboratory of Adversarial Artificial Intelligence, Shenzhen, China +2
SAVE: Speech-Aware Video Representation Learning for Video-Text Retrieval
Mar 9, 2026Ruixiang Zhao, Zhihao Xu, Bangxiang Lan +3Video-Text Retrieval Audio-Visual Representation Learning
Renmin University of China
VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval
Feb 8, 2026Issar Tzachor, Dvir Samuel, Rami Ben-AriVideo-Text Retrieval Vision-Language Retrieval
OriginAI, Israel.

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

Shahaf Wagner, Gabriele Serussi, Dan Ben Ami, Tomer Galanti+1

INSIGHT Lab, Ben-Gurion University of the Negev, Israel · Decart AI · Texas A&M University, College Station

Video-Text Retrieval Video Retrieval Vision-Language Retrieval Video Token Compression

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Finding the right video often requires distinguishing similar scenes in which different events occur. Joint matching improves retrieval, but processing rich video representations for each query is costly. VEDJE compresses features within sampled frames while keeping their representations separate in a reusable cache. Feature-change prediction supplies an auxiliary training signal that improves retrieval from the compressed cache without adding work at query time. On MSR-VTT, MSVD, DiDeMo, and ActivityNet, VEDJE improves R@1 over matched first-stage retrievers in both retrieval directions. On MSR-VTT, it reaches 59.8 text-to-video R@1 with a fine-tuned VideoCLIP-XL first stage. In the VideoPrism configuration, shrinking the per-video cache fourfold to 12 KiB preserves text-to-video recall within 0.2 points. These results show that accurate video search can operate on compact evidence, encoded once and reused as new queries arrive.