Search Results for author: Kaiyang Zhou

Found 14 papers, 11 papers with code

Learning to Prompt for Vision-Language Models

1 code implementation2 Sep 2021 Kaiyang Zhou, Jingkang Yang, Chen Change Loy, Ziwei Liu

It shifts from the tradition of using images and discrete labels for learning a fixed set of weights, seen as visual concepts, to aligning images and raw text for two separate encoders.

Representation Learning

Energy-Based Open-World Uncertainty Modeling for Confidence Calibration

no code implementations27 Jul 2021 Yezhen Wang, Bo Li, Tong Che, Kaiyang Zhou, Ziwei Liu, Dongsheng Li

Confidence calibration is of great importance to the reliability of decisions made by machine learning systems.

MixStyle Neural Networks for Domain Generalization and Adaptation

2 code implementations5 Jul 2021 Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

In this work, we address domain generalization with MixStyle, a plug-and-play, parameter-free module that is simply inserted to shallow CNN layers and requires no modification to training objectives.

Domain Generalization Object Recognition +1

Semi-Supervised Domain Generalization with Stochastic StyleMatch

2 code implementations1 Jun 2021 Kaiyang Zhou, Chen Change Loy, Ziwei Liu

Our proposed approach, StyleMatch, is inspired by FixMatch, a state-of-the-art semi-supervised learning method based on pseudo-labeling, with several new ingredients tailored to solve SSDG.

Domain Generalization

Domain Generalization with MixStyle

2 code implementations ICLR 2021 Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e. g., photo vs.~sketch images).

Domain Generalization

Domain Generalization in Vision: A Survey

1 code implementation3 Mar 2021 Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, Chen Change Loy

In particular, intensive research in this topic has led to a broad spectrum of methodologies, e. g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few; and has covered various vision applications such as object recognition, segmentation, action recognition, and person re-identification.

Action Recognition Data Augmentation +6

Learning to Generate Novel Domains for Domain Generalization

1 code implementation ECCV 2020 Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, Tao Xiang

This explicitly increases the diversity of available training domains and leads to a more generalizable model.

Domain Generalization

Domain Adaptive Ensemble Learning

1 code implementation16 Mar 2020 Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

Each such classifier is an expert to its own domain and a non-expert to others.

Domain Generalization Ensemble Learning +2

Deep Domain-Adversarial Image Generation for Domain Generalisation

no code implementations12 Mar 2020 Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, Tao Xiang

This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier.

Image Generation

Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch

2 code implementations22 Oct 2019 Kaiyang Zhou, Tao Xiang

Person re-identification (re-ID), which aims to re-identify people across different camera views, has been significantly advanced by deep learning in recent years, particularly with convolutional neural networks (CNNs).

Person Re-Identification

Learning Generalisable Omni-Scale Representations for Person Re-Identification

2 code implementations15 Oct 2019 Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, Tao Xiang

An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.

Unsupervised Domain Adaptation Unsupervised Person Re-Identification

Omni-Scale Feature Learning for Person Re-Identification

5 code implementations ICCV 2019 Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, Tao Xiang

As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales.

Person Re-Identification

Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

6 code implementations29 Dec 2017 Kaiyang Zhou, Yu Qiao, Tao Xiang

Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos.

Decision Making Supervised Video Summarization +1

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