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Found 8 papers, 4 papers with code
TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible Adapter
In situations involving system upgrades that require updating the upstream foundation model, it becomes essential to re-train all downstream modules to adapt to the new foundation model, which is inflexible and inefficient.
Darwinian Model Upgrades: Model Evolving with Selective Compatibility
The traditional model upgrading paradigm for retrieval requires recomputing all gallery embeddings before deploying the new model (dubbed as "backfilling"), which is quite expensive and time-consuming considering billions of instances in industrial applications.
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval
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.
Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval
Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval.
Towards Universal Backward-Compatible Representation Learning
The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features.
Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval
In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly.
Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval
In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly.
A Simple Yet Effective Method for Video Temporal Grounding with Cross-Modality Attention
The task of language-guided video temporal grounding is to localize the particular video clip corresponding to a query sentence in an untrimmed video.
