Search Results for author: Nannan Wang

Found 32 papers, 4 papers with code

Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id

no code implementations29 Sep 2021 De Cheng, Jingyu Zhou, Nannan Wang, Xinbo Gao

However, since person Re-Id is an open-set problem, the clustering based methods often leave out lots of outlier instances or group the instances into the wrong clusters, thus they can not make full use of the training samples as a whole.

Contrastive Learning Metric Learning +2

Single Image Dehazing with An Independent Detail-Recovery Network

1 code implementation22 Sep 2021 Yan Li, De Cheng, Jiande Sun, Dingwen Zhang, Nannan Wang, Xinbo Gao

In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.

Image Dehazing Single Image Dehazing

Modeling Adversarial Noise for Adversarial Defense

no code implementations21 Sep 2021 Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu

Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks.

Adversarial Defense

Support-Set Based Cross-Supervision for Video Grounding

no code implementations ICCV 2021 Xinpeng Ding, Nannan Wang, Shiwei Zhang, De Cheng, Xiaomeng Li, Ziyuan Huang, Mingqian Tang, Xinbo Gao

The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.

Contrastive Learning

Exploring Set Similarity for Dense Self-supervised Representation Learning

no code implementations19 Jul 2021 Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu

By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks.

Instance Segmentation Keypoint Detection +3

Kernel Mean Estimation by Marginalized Corrupted Distributions

no code implementations10 Jul 2021 Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu

Estimating the kernel mean in a reproducing kernel Hilbert space is a critical component in many kernel learning algorithms.

Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training

no code implementations10 Jun 2021 Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Jun Yu, Xiaoyu Wang, Tongliang Liu

However, pre-processing methods may suffer from the robustness degradation effect, in which the defense reduces rather than improving the adversarial robustness of a target model in a white-box setting.

Adversarial Defense Adversarial Robustness

Towards Defending against Adversarial Examples via Attack-Invariant Features

no code implementations9 Jun 2021 Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, Xinbo Gao

However, given the continuously evolving attacks, models trained on seen types of adversarial examples generally cannot generalize well to unseen types of adversarial examples.

Adversarial Robustness

Removing Adversarial Noise in Class Activation Feature Space

no code implementations ICCV 2021 Dawei Zhou, Nannan Wang, Chunlei Peng, Xinbo Gao, Xiaoyu Wang, Jun Yu, Tongliang Liu

Then, we train a denoising model to minimize the distances between the adversarial examples and the natural examples in the class activation feature space.

Adversarial Robustness Denoising

Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer

2 code implementations CVPR 2021 Tianwei Lin, Zhuoqi Ma, Fu Li, Dongliang He, Xin Li, Errui Ding, Nannan Wang, Jie Li, Xinbo Gao

Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle).

Style Transfer

Syncretic Modality Collaborative Learning for Visible Infrared Person Re-Identification

no code implementations ICCV 2021 Ziyu Wei, Xi Yang, Nannan Wang, Xinbo Gao

Visible infrared person re-identification (VI-REID) aims to match pedestrian images between the daytime visible and nighttime infrared camera views.

Person Re-Identification

ADD-Defense: Towards Defending Widespread Adversarial Examples via Perturbation-Invariant Representation

no code implementations1 Jan 2021 Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Xinbo Gao

Motivated by this observation, we propose a defense framework ADD-Defense, which extracts the invariant information called \textit{perturbation-invariant representation} (PIR) to defend against widespread adversarial examples.

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels

no code implementations2 Dec 2020 Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao

The traditional transition matrix is limited to model closed-set label noise, where noisy training data has true class labels within the noisy label set.

Class2Simi: A New Perspective on Learning with Label Noise

no code implementations28 Sep 2020 Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

It is worthwhile to perform the transformation: We prove that the noise rate for the noisy similarity labels is lower than that of the noisy class labels, because similarity labels themselves are robust to noise.

CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles

no code implementations24 Sep 2020 Jingda Guo, Dominic Carrillo, Sihai Tang, Qi Chen, Qing Yang, Song Fu, Xi Wang, Nannan Wang, Paparao Palacharla

To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles.

3D Object Detection Autonomous Vehicles

Part-dependent Label Noise: Towards Instance-dependent Label Noise

1 code implementation NeurIPS 2020 Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, DaCheng Tao, Masashi Sugiyama

Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise.

Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels

no code implementations14 Jun 2020 Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

To give an affirmative answer, in this paper, we propose a framework called Class2Simi: it transforms data points with noisy class labels to data pairs with noisy similarity labels, where a similarity label denotes whether a pair shares the class label or not.

Contrastive Learning Learning with noisy labels +1

Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition

no code implementations25 May 2020 Bing Cao, Nannan Wang, Xinbo Gao, Jie Li, Zhifeng Li

Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios.

Face Recognition Heterogeneous Face Recognition +1

Multi-Class Classification from Noisy-Similarity-Labeled Data

no code implementations16 Feb 2020 Songhua Wu, Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu

We further estimate the transition matrix from only noisy data and build a novel learning system to learn a classifier which can assign noise-free class labels for instances.

General Classification Multi-class Classification

Facial Attribute Capsules for Noise Face Super Resolution

no code implementations16 Feb 2020 Jingwei Xin, Nannan Wang, Xinrui Jiang, Jie Li, Xinbo Gao, Zhifeng Li

In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs.

Image Super-Resolution

Video Face Super-Resolution with Motion-Adaptive Feedback Cell

no code implementations15 Feb 2020 Jingwei Xin, Nannan Wang, Jie Li, Xinbo Gao, Zhifeng Li

Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames.

Motion Compensation Motion Estimation +2

Are Anchor Points Really Indispensable in Label-Noise Learning?

1 code implementation NeurIPS 2019 Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama

Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points} (i. e., data points that belong to a specific class almost surely).

Learning with noisy labels

Saliency deep embedding for aurora image search

no code implementations23 May 2018 Xi Yang, Xinbo Gao, Bin Song, Nannan Wang, Dong Yang

In this paper, we aim to explore a new search method for images captured with circular fisheye lens, especially the aurora images.

Image Retrieval Region Proposal

Random Sampling for Fast Face Sketch Synthesis

no code implementations8 Jan 2017 Nannan Wang, Xinbo Gao, Jie Li

The most time-consuming or main computation complexity for exemplar-based face sketch synthesis methods lies in the neighbor selection process.

Face Hallucination Face Sketch Synthesis

Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition

no code implementations1 Jul 2016 Chunlei Peng, Xinbo Gao, Nannan Wang, Jie Li

An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors.

Face Recognition Heterogeneous Face Recognition

Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics

no code implementations25 Mar 2016 Nannan Wang, Jie Li, Leiyu Sun, Bin Song, Xinbo Gao

In this paper, we proposed a synthesized face sketch recognition framework based on full-reference image quality assessment metrics.

Face Recognition Face Sketch Synthesis +2

Graphical Representation for Heterogeneous Face Recognition

no code implementations2 Mar 2015 Chunlei Peng, Xinbo Gao, Nannan Wang, Jie Li

Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i. e., different sensors or different wavelengths) for identification.

Face Recognition Heterogeneous Face Recognition

Facial Feature Point Detection: A Comprehensive Survey

no code implementations4 Oct 2014 Nannan Wang, Xinbo Gao, DaCheng Tao, Xuelong. Li

CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point.

3D FACE MODELING Face Alignment +2

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