Search Results for author: Yan Bai

Found 16 papers, 3 papers with code

Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation

no code implementations8 Dec 2022 Yulu Gan, Yan Bai, Yihang Lou, Xianzheng Ma, Renrui Zhang, Nian Shi, Lin Luo

Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions.

Test-time Adaptation

Bayesian Evidential Learning for Few-Shot Classification

no code implementations19 Jul 2022 Xiongkun Linghu, Yan Bai, Yihang Lou, Shengsen Wu, Jinze Li, Jianzhong He, Tao Bai

Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.

Classification Metric Learning +1

Trusted Multi-Scale Classification Framework for Whole Slide Image

no code implementations12 Jul 2022 Ming Feng, Kele Xu, Nanhui Wu, Weiquan Huang, Yan Bai, Changjian Wang, Huaimin Wang

Leveraging the Vision Transformer as the backbone for multi branches, our framework can jointly classification modeling, estimating the uncertainty of each magnification of a microscope and integrate the evidence from different magnification.


Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning

no code implementations3 Jul 2022 Jinze Li, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Shaoyun Xu, Tao Bai

The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem.

Few-Shot Class-Incremental Learning Incremental Learning

Switchable Representation Learning Framework with Self-compatibility

no code implementations CVPR 2023 Shengsen Wu, Yan Bai, Yihang Lou, Xiongkun Linghu, Jianzhong He, Ling-Yu Duan

Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models.

Representation Learning

Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation

no code implementations7 Aug 2021 Shengsen Wu, Liang Chen, Yihang Lou, Yan Bai, Tao Bai, Minghua Deng, Lingyu Duan

Therefore, backward-compatible representation is proposed to enable "new" features to be compared with "old" features directly, which means that the database is active when there are both "new" and "old" features in it.

Contrastive Learning

Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning

1 code implementation6 Aug 2021 Yan Bai, Jile Jiao, Shengsen Wu, Yihang Lou, Jun Liu, Xuetao Feng, Ling-Yu Duan

It is a heavy workload to re-extract features of the whole database every time. Feature compatibility enables the learned new visual features to be directly compared with the old features stored in the database.


Large-Scale Unsupervised Person Re-Identification with Contrastive Learning

no code implementations17 May 2021 Weiquan Huang, Yan Bai, Qiuyu Ren, Xinbo Zhao, Ming Feng, Yin Wang

In particular, most existing unsupervised and domain adaptation ReID methods utilize only the public datasets in their experiments, with labels removed.

Contrastive Learning Domain Adaptation +3

Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification

1 code implementation26 Dec 2020 Yongxing Dai, Jun Liu, Yan Bai, Zekun Tong, Ling-Yu Duan

To this end, we propose a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase, to alternatively boost the label purity and feature discriminability in the target domain for more reliable re-ID.

Clustering Domain Adaptive Person Re-Identification +1

Spherical Feature Transform for Deep Metric Learning

no code implementations ECCV 2020 Yuke Zhu, Yan Bai, Yichen Wei

Consequently, the feature transform is performed by a rotation that respects the spherical data distributions.

Data Augmentation Metric Learning +2

Prime-Aware Adaptive Distillation

1 code implementation ECCV 2020 Youcai Zhang, Zhonghao Lan, Yuchen Dai, Fangao Zeng, Yan Bai, Jie Chang, Yichen Wei

With ten teacher-student combinations on six datasets, PAD promotes the performance of existing distillation methods and outperforms recent state-of-the-art methods.

Knowledge Distillation Metric Learning +2

Compact Descriptors for Video Analysis: the Emerging MPEG Standard

no code implementations26 Apr 2017 Ling-Yu Duan, Vijay Chandrasekhar, Shiqi Wang, Yihang Lou, Jie Lin, Yan Bai, Tiejun Huang, Alex ChiChung Kot, Wen Gao

This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).

Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

no code implementations1 Mar 2017 Yan Bai, Feng Gao, Yihang Lou, Shiqi Wang, Tiejun Huang, Ling-Yu Duan

In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition.

Fine-Grained Visual Recognition Metric Learning +1

Improving Object Detection with Region Similarity Learning

no code implementations1 Mar 2017 Feng Gao, Yihang Lou, Yan Bai, Shiqi Wang, Tiejun Huang, Ling-Yu Duan

Object detection aims to identify instances of semantic objects of a certain class in images or videos.

Multi-Task Learning Object +2

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