In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.
Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains.
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains.
Ranked #1 on Multi-target Domain Adaptation on Office-Home
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data.
In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains.
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes.
This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively.
Ranked #6 on Unsupervised Domain Adaptation on Market to Duke
This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain.
Ranked #6 on Unsupervised Domain Adaptation on Duke to MSMT
To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties.
For training of deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data.
Person re-identification (re-ID) poses unique challenges for unsupervised domain adaptation (UDA) in that classes in the source and target sets (domains) are entirely different and that image variations are largely caused by cameras.
This paper considers the task of thorax disease classification on chest X-ray images.
In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation.
Ranked #57 on Person Re-Identification on DukeMTMC-reID
In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
Ranked #4 on Image Classification on Fashion-MNIST
Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance.
Ranked #8 on Person Re-Identification on CUHK03
In this paper, we propose a semantic, class-specific approach to re-rank object proposals, which can consistently improve the recall performance even with less proposals.
Secondly, we present a ground control points selection scheme according to the maximum matching confidence of each pixel.
However, when the data instances of a target domain have a different distribution than that of a source domain, the dictionary learning method may fail to perform well.