Search Results for author: Xuyuan Xu

Found 11 papers, 8 papers with code

PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas

1 code implementation26 Jun 2023 Chen Li, Xutan Peng, Teng Wang, Yixiao Ge, Mengyang Liu, Xuyuan Xu, Yexin Wang, Ying Shan

Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently.

Genre classification Retrieval +1

TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible Adapter

no code implementations22 Jun 2023 Binjie Zhang, Yixiao Ge, Xuyuan Xu, Ying Shan, Mike Zheng Shou

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.

Question Answering Retrieval +5

Binary Embedding-based Retrieval at Tencent

1 code implementation17 Feb 2023 Yukang Gan, Yixiao Ge, Chang Zhou, Shupeng Su, Zhouchuan Xu, Xuyuan Xu, Quanchao Hui, Xiang Chen, Yexin Wang, Ying Shan

To tackle the challenge, we propose a binary embedding-based retrieval (BEBR) engine equipped with a recurrent binarization algorithm that enables customized bits per dimension.

Binarization Retrieval

Darwinian Model Upgrades: Model Evolving with Selective Compatibility

no code implementations13 Oct 2022 Binjie Zhang, Shupeng Su, Yixiao Ge, Xuyuan Xu, Yexin Wang, Chun Yuan, Mike Zheng Shou, Ying Shan

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.

Face Recognition Retrieval

Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval

1 code implementation29 Apr 2022 Shupeng Su, Binjie Zhang, Yixiao Ge, Xuyuan Xu, Yexin Wang, Chun Yuan, Ying Shan

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.

Image Retrieval Privacy Preserving +1

Towards Universal Backward-Compatible Representation Learning

2 code implementations3 Mar 2022 Binjie Zhang, Yixiao Ge, Yantao Shen, Shupeng Su, Fanzi Wu, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan

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.

Face Recognition Representation Learning

Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval

1 code implementation24 Jan 2022 Binjie Zhang, Yixiao Ge, Yantao Shen, Yu Li, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan

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.

Image Retrieval regression +1

Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation

1 code implementation16 Dec 2021 Yujia Zhang, Lai-Man Po, Xuyuan Xu, Mengyang Liu, Yexin Wang, Weifeng Ou, Yuzhi Zhao, Wing-Yin Yu

Moreover, we employ a joint optimization combining pretext tasks with contrastive learning to further enhance the spatio-temporal representation learning.

Contrastive Learning Representation Learning +1

3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity Challenge

1 code implementation4 Dec 2021 Xinlong Sun, Yangyang Qin, Xuyuan Xu, Guoping Gong, Yang Fang, Yexin Wang

As a basic task of computer vision, image similarity retrieval is facing the challenge of large-scale data and image copy attacks.

Retrieval Self-Supervised Learning

Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval

no code implementations ICLR 2022 Binjie Zhang, Yixiao Ge, Yantao Shen, Yu Li, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan

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.

Image Retrieval regression +1

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