1 code implementation • 11 May 2023 • Zhaoyang Zhang, Yantao Shen, Kunyu Shi, Zhaowei Cai, Jun Fang, Siqi Deng, Hao Yang, Davide Modolo, Zhuowen Tu, Stefano Soatto
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer.
no code implementations • 12 May 2022 • Yue Zhao, Yantao Shen, Yuanjun Xiong, Shuo Yang, Wei Xia, Zhuowen Tu, Bernt Schiele, Stefano Soatto
We present a method to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model.
2 code implementations • 3 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.
1 code implementation • 24 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.
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.
no code implementations • 1 Jan 2021 • Yantao Shen, Fanzi Wu, Ying Shan
In this work, we introduce an approach for feature compatible learning without inheriting old classifier and training data, i. e., Non-Inherent Feature Compatible Learning.
3 code implementations • CVPR 2020 • Yantao Shen, Yuanjun Xiong, Wei Xia, Stefano Soatto
Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods.
no code implementations • ECCV 2018 • Dapeng Chen, Hongsheng Li, Xihui Liu, Yantao Shen, Zejian yuan, Xiaogang Wang
Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities.
Ranked #22 on Text based Person Retrieval on CUHK-PEDES
1 code implementation • CVPR 2018 • Yantao Shen, Tong Xiao, Hongsheng Li, Shuai Yi, Xiaogang Wang
Person re-identification aims to robustly measure similarities between person images.
1 code implementation • CVPR 2018 • Yantao Shen, Hongsheng Li, Tong Xiao, Shuai Yi, Dapeng Chen, Xiaogang Wang
Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images.
no code implementations • ECCV 2018 • Yantao Shen, Hongsheng Li, Shuai Yi, Dapeng Chen, Xiaogang Wang
However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs.
Ranked #2 on Person Re-Identification on CUHK03
no code implementations • ICCV 2017 • Yantao Shen, Tong Xiao, Hongsheng Li, Shuai Yi, Xiaogang Wang
Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation.