5 citations · 11 across the 4 of their papers we have counts for
5 papers
Darwinian Model Upgrades: Model Evolving with Selective Compatibility
Binjie Zhang, Shupeng Su, Yixiao Ge +5
The traditional model upgrading paradigm for retrieval requires recomputing all gallery embeddings before deploying the new model (dubbed as "backfilling"), which is quite expensiv…
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval
Shupeng Su, Binjie Zhang, Yixiao Ge +4
The task of privacy-preserving model upgrades in image retrieval desires to reap the benefits of rapidly evolving new models without accessing the raw gallery images. A pioneering…
Towards Universal Backward-Compatible Representation Learning
Binjie Zhang, Yixiao Ge, Yantao Shen +6
Conventional model upgrades for visual search systems require offline refresh of gallery features by feeding gallery images into new models (dubbed as "backfill"), which is time-co…
Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval
Binjie Zhang, Yixiao Ge, Yantao Shen +5
The task of hot-refresh model upgrades of image retrieval systems plays an essential role in the industry but has never been investigated in academia before. Conventional cold-refr…
A Simple Yet Effective Method for Video Temporal Grounding with Cross-Modality Attention
Binjie Zhang, Yu Li, Chun Yuan +3
The task of language-guided video temporal grounding is to localize the particular video clip corresponding to a query sentence in an untrimmed video. Though progress has been made…