Search Results for author: Cuiling Lan

Found 43 papers, 13 papers with code

ReSTR: Convolution-free Referring Image Segmentation Using Transformers

no code implementations CVPR 2022 Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, Suha Kwak

Most of existing methods for this task rely heavily on convolutional neural networks, which however have trouble capturing long-range dependencies between entities in the language expression and are not flexible enough for modeling interactions between the two different modalities.

Semantic Segmentation

Deep Frequency Filtering for Domain Generalization

no code implementations23 Mar 2022 Shiqi Lin, Zhizheng Zhang, Zhipeng Huang, Yan Lu, Cuiling Lan, Peng Chu, Quanzeng You, Jiang Wang, Zicheng Liu, Amey Parulkar, Viraj Navkal, Zhibo Chen

Improving the generalization capability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge.

Domain Generalization

Mask-based Latent Reconstruction for Reinforcement Learning

no code implementations28 Jan 2022 Tao Yu, Zhizheng Zhang, Cuiling Lan, Yan Lu, Zhibo Chen

For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance.

reinforcement-learning Representation Learning

Confounder Identification-free Causal Visual Feature Learning

no code implementations26 Nov 2021 Xin Li, Zhizheng Zhang, Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Xin Jin, Zhibo Chen

In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders.

Domain Generalization Meta-Learning

WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation

no code implementations29 Sep 2021 Namyup Kim, Taeyoung Son, Cuiling Lan, Wenjun Zeng, Suha Kwak

We also present a method which injects the style representation of the web-crawled data into the source domain on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training.

Domain Generalization Semantic Segmentation

ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation

1 code implementation NeurIPS 2021 Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen

Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain.

Unsupervised Domain Adaptation

MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation

1 code implementation CVPR 2021 Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhibo Chen

For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics.

Classification General Classification +4

Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification

no code implementations25 Mar 2021 Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Quanzeng You, Zicheng Liu, Kecheng Zheng, Zhibo Chen

Each recomposed feature, obtained based on the domain-invariant feature (which enables a reliable inheritance of identity) and an enhancement from a domain specific feature (which enables the approximation of real distributions), is thus an "ideal" augmentation.

Disentanglement Domain Adaptive Person Re-Identification +1

Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation

no code implementations ICCV 2021 Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen

Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a domain discriminator in order to have aligned feature distributions.

Unsupervised Domain Adaptation

Generalizing to Unseen Domains: A Survey on Domain Generalization

1 code implementation2 Mar 2021 Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, Philip S. Yu

Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.

Domain Generalization Out-of-Distribution Generalization +1

AttributeNet: Attribute Enhanced Vehicle Re-Identification

no code implementations7 Feb 2021 Rodolfo Quispe, Cuiling Lan, Wenjun Zeng, Helio Pedrini

Vehicle Re-Identification (V-ReID) is a critical task that associates the same vehicle across images from different camera viewpoints.

Vehicle Re-Identification

Style Normalization and Restitution for Domain Generalization and Adaptation

1 code implementation3 Jan 2021 Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen

In this paper, we design a novel Style Normalization and Restitution module (SNR) to simultaneously ensure both high generalization and discrimination capability of the networks.

Computer Vision Disentanglement +5

Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification

1 code implementation16 Dec 2020 Kecheng Zheng, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zheng-Jun Zha

Based on this finding, we propose to exploit the uncertainty (measured by consistency levels) to evaluate the reliability of the pseudo-label of a sample and incorporate the uncertainty to re-weight its contribution within various ReID losses, including the identity (ID) classification loss per sample, the triplet loss, and the contrastive loss.

Domain Adaptive Person Re-Identification Person Re-Identification +1

Uncertainty-Aware Few-Shot Image Classification

no code implementations9 Oct 2020 Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Zhibo Chen, Shih-Fu Chang

In this work, we propose Uncertainty-Aware Few-Shot framework for image classification by modeling uncertainty of the similarities of query-support pairs and performing uncertainty-aware optimization.

Classification Few-Shot Image Classification +2

Feature Alignment and Restoration for Domain Generalization and Adaptation

no code implementations22 Jun 2020 Xin Jin, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen

To ensure high discrimination, we propose a Feature Restoration (FR) operation to distill task-relevant features from the residual information and use them to compensate for the aligned features.

Disentanglement Domain Generalization +1

Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification

no code implementations8 Jun 2020 Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen, Shih-Fu Chang

There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID.

Classification General Classification +3

Global Distance-distributions Separation for Unsupervised Person Re-identification

no code implementations ECCV 2020 Xin Jin, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen

To address this problem, we introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view.

Domain Adaptation POS +1

Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification

no code implementations CVPR 2020 Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen

In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation.

Video-Based Person Re-Identification

FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Optical Flow Estimation

no code implementations17 Jan 2020 Xiaolin Song, Yuyang Zhao, Jingyu Yang, Cuiling Lan, Wenjun Zeng

To exploit such flexible and comprehensive information, we propose a semi-supervised Feature Pyramidal Correlation and Residual Reconstruction Network (FPCR-Net) for optical flow estimation from frame pairs.

Optical Flow Estimation

Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification

no code implementations15 Jan 2020 Xin Jin, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen

To the best of our knowledge, we are the first to make use of multi-shots of an object in a teacher-student learning manner for effectively boosting the single image based re-id.

Knowledge Distillation

EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks

no code implementations3 Sep 2019 Pengfei Zhang, Jianru Xue, Cuiling Lan, Wen-Jun Zeng, Zhanning Gao, Nanning Zheng

For an RNN block, an EleAttG is used for adaptively modulating the input by assigning different levels of importance, i. e., attention, to each element/dimension of the input.

Action Recognition Skeleton Based Action Recognition

Semantics-Aligned Representation Learning for Person Re-identification

1 code implementation30 May 2019 Xin Jin, Cuiling Lan, Wen-Jun Zeng, Guoqiang Wei, Zhibo Chen

Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image.

Person Re-Identification Representation Learning +1

Relation-Aware Global Attention for Person Re-identification

1 code implementation CVPR 2020 Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Xin Jin, Zhibo Chen

For person re-identification (re-id), attention mechanisms have become attractive as they aim at strengthening discriminative features and suppressing irrelevant ones, which matches well the key of re-id, i. e., discriminative feature learning.

Image Classification Person Re-Identification +1

Target-Tailored Source-Transformation for Scene Graph Generation

no code implementations3 Apr 2019 Wentong Liao, Cuiling Lan, Wen-Jun Zeng, Michael Ying Yang, Bodo Rosenhahn

We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation.

graph construction Graph Generation +4

View Invariant 3D Human Pose Estimation

no code implementations30 Jan 2019 Guoqiang Wei, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen

The diversity of capturing viewpoints and the flexibility of the human poses, however, remain some significant challenges.

3D Human Pose Estimation 3D Pose Estimation

Temporal-Spatial Mapping for Action Recognition

no code implementations11 Sep 2018 Xiaolin Song, Cuiling Lan, Wen-Jun Zeng, Junliang Xing, Jingyu Yang, Xiaoyan Sun

We propose a video level 2D feature representation by transforming the convolutional features of all frames to a 2D feature map, referred to as VideoMap.

Action Recognition Computer Vision +3

Adding Attentiveness to the Neurons in Recurrent Neural Networks

no code implementations ECCV 2018 Pengfei Zhang, Jianru Xue, Cuiling Lan, Wen-Jun Zeng, Zhanning Gao, Nanning Zheng

We propose adding a simple yet effective Element-wiseAttention Gate (EleAttG) to an RNN block (e. g., all RNN neurons in a network layer) that empowers the RNN neurons to have the attentiveness capability.

Action Recognition Skeleton Based Action Recognition

View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition

2 code implementations20 Apr 2018 Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng

In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints in a learning based data driven manner.

Action Recognition Skeleton Based Action Recognition

Human Pose Estimation using Global and Local Normalization

no code implementations ICCV 2017 Ke Sun, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Dong Liu, Jingdong Wang

We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation.

Pose Estimation

View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data

1 code implementation ICCV 2017 Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng

Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.

Action Recognition Skeleton Based Action Recognition

Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

no code implementations24 Mar 2016 Wentao Zhu, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Yanghao Li, Li Shen, Xiaohui Xie

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions.

Action Recognition Skeleton Based Action Recognition

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