Search Results for author: Xinmei Tian

Found 28 papers, 12 papers with code

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

no code implementations ICML 2020 Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian

To obtain sufficient knowledge for crafting adversarial examples, previous methods query the target model with inputs that are perturbed with different searching directions.

Prompt Distribution Learning

no code implementations6 May 2022 Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, Xinmei Tian

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks.

Language Modelling

Transferrable Contrastive Learning for Visual Domain Adaptation

no code implementations14 Dec 2021 Yang Chen, Yingwei Pan, Yu Wang, Ting Yao, Xinmei Tian, Tao Mei

From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i. e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently.

Contrastive Learning Domain Adaptation +1

A Style and Semantic Memory Mechanism for Domain Generalization

no code implementations ICCV 2021 Yang Chen, Yu Wang, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei

Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains.

Domain Generalization

Class-Disentanglement and Applications in Adversarial Detection and Defense

no code implementations NeurIPS 2021 Kaiwen Yang, Tianyi Zhou, Yonggang Zhang, Xinmei Tian, DaCheng Tao

In this paper, we propose ''class-disentanglement'' that trains a variational autoencoder $G(\cdot)$ to extract this class-dependent information as $x - G(x)$ via a trade-off between reconstructing $x$ by $G(x)$ and classifying $x$ by $D(x-G(x))$, where the former competes with the latter in decomposing $x$ so the latter retains only necessary information for classification in $x-G(x)$.

Adversarial Defense Disentanglement

Identity-Disentangled Adversarial Augmentation for Self-supervised Learning

no code implementations29 Sep 2021 Kaiwen Yang, Tianyi Zhou, Xinmei Tian, DaCheng Tao

We then adversarially perturb $G(x)$ in the VAE's bottleneck space and adds it back to the original $R(x)$ as an augmentation, which is therefore sufficiently challenging for contrastive learning and meanwhile preserves the sample identity intact.

Contrastive Learning Data Augmentation +1

Revisiting Knowledge Distillation: An Inheritance and Exploration Framework

1 code implementation CVPR 2021 Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua

The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss.

Knowledge Distillation

Domain-Class Correlation Decomposition for Generalizable Person Re-Identification

no code implementations29 Jun 2021 Kaiwen Yang, Xinmei Tian

Domain adversarial learning is a promising domain generalization method that aims to remove domain information in the latent representation through adversarial training.

Domain Generalization Generalizable Person Re-identification

Adversarial Robustness through the Lens of Causality

no code implementations ICLR 2022 Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang

The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution.

Adversarial Attack Adversarial Robustness

3D Local Convolutional Neural Networks for Gait Recognition

1 code implementation ICCV 2021 Zhen Huang, Dixiu Xue, Xu Shen, Xinmei Tian, Houqiang Li, Jianqiang Huang, Xian-Sheng Hua

Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales.

Frame Gait Recognition

Learning to Localize Actions from Moments

1 code implementation ECCV 2020 Fuchen Long, Ting Yao, Zhaofan Qiu, Xinmei Tian, Jiebo Luo, Tao Mei

In this paper, we introduce a new design of transfer learning type to learn action localization for a large set of action categories, but only on action moments from the categories of interest and temporal annotations of untrimmed videos from a small set of action classes.

Action Localization Transfer Learning

Continuous Dropout

1 code implementation28 Nov 2019 Xu Shen, Xinmei Tian, Tongliang Liu, Fang Xu, DaCheng Tao

On the one hand, continuous dropout is considerably closer to the activation characteristics of neurons in the human brain than traditional binary dropout.

Transform-Invariant Convolutional Neural Networks for Image Classification and Search

1 code implementation28 Nov 2019 Xu Shen, Xinmei Tian, Anfeng He, Shaoyan Sun, DaCheng Tao

In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage.

General Classification Image Classification +2

Quantization Networks

1 code implementation CVPR 2019 Jiwei Yang, Xu Shen, Jun Xing, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua

The proposed quantization function can be learned in a lossless and end-to-end manner and works for any weights and activations of neural networks in a simple and uniform way.

Image Classification Object Detection +1

Mocycle-GAN: Unpaired Video-to-Video Translation

no code implementations26 Aug 2019 Yang Chen, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei

Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications.

Frame Motion Estimation +2

Learning Spatio-Temporal Representation with Local and Global Diffusion

no code implementations CVPR 2019 Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Xinmei Tian, Tao Mei

Diffusions effectively interact two aspects of information, i. e., localized and holistic, for more powerful way of representation learning.

Action Classification Action Detection +3

Exploring Object Relation in Mean Teacher for Cross-Domain Detection

1 code implementation CVPR 2019 Qi Cai, Yingwei Pan, Chong-Wah Ngo, Xinmei Tian, Ling-Yu Duan, Ting Yao

The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student.

Unsupervised Domain Adaptation

Camera Lens Super-Resolution

1 code implementation CVPR 2019 Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu

Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling.

Image Super-Resolution

On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning

no code implementations3 Apr 2019 Ya Li, Xinmei Tian, Tongliang Liu, DaCheng Tao

The objective of our proposed method is to transform the features from different tasks into a common feature space in which the tasks are closely related and the shared parameters can be better optimized.

Multi-Task Learning

Towards a Better Match in Siamese Network Based Visual Object Tracker

no code implementations5 Sep 2018 Anfeng He, Chong Luo, Xinmei Tian, Wen-Jun Zeng

Recently, Siamese network based trackers have received tremendous interest for their fast tracking speed and high performance.

Visual Object Tracking

Deep Domain Generalization via Conditional Invariant Adversarial Networks

no code implementations ECCV 2018 Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, DaCheng Tao

Under the assumption that the conditional distribution $P(Y|X)$ remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation $T(X)$ by minimizing the discrepancy of the marginal distribution $P(T(X))$.

Domain Generalization Representation Learning

Domain Generalization via Conditional Invariant Representation

1 code implementation23 Jul 2018 Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, DaCheng Tao

With the conditional invariant representation, the invariance of the joint distribution $\mathbb{P}(h(X), Y)$ can be guaranteed if the class prior $\mathbb{P}(Y)$ does not change across training and test domains.

Domain Generalization

Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition

no code implementations16 May 2018 Xikun Zhang, Chang Xu, Xinmei Tian, DaCheng Tao

Considering the complementarity between graph node convolution and graph edge convolution, we additionally construct two hybrid neural networks to combine graph node convolutional neural network and graph edge convolutional neural network using shared intermediate layers.

Action Recognition Pose Estimation +1

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