Search Results for author: Wen Li

Found 55 papers, 20 papers with code

Fixing Localization Errors to Improve Image Classification

1 code implementation ECCV 2020 Guolei Sun, Salman Khan, Wen Li, Hisham Cholakkal, Fahad Shahbaz Khan, Luc van Gool

This way, in an effort to fix localization errors, our loss provides an extra supervisory signal that helps the model to better discriminate between similar classes.

Classification General Classification +3

Revisiting Random Channel Pruning for Neural Network Compression

no code implementations11 May 2022 Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc van Gool

The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning.

Neural Network Compression

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation

1 code implementation12 Apr 2022 Yahao Liu, Jinhong Deng, Jiale Tao, Tong Chu, Lixin Duan, Wen Li

Existing works typically treat cross-domain semantic segmentation (CDSS) as a data distribution mismatch problem and focus on aligning the marginal distribution or conditional distribution.

Semantic Segmentation

Structure-Aware Motion Transfer with Deformable Anchor Model

1 code implementation11 Apr 2022 Jiale Tao, Biao Wang, Borun Xu, Tiezheng Ge, Yuning Jiang, Wen Li, Lixin Duan

Specifically, inspired by the known deformable part model (DPM), our DAM introduces two types of anchors or keypoints: i) a number of motion anchors that capture both appearance and motion information from the source image and driving video; ii) a latent root anchor, which is linked to the motion anchors to facilitate better learning of the representations of the object structure information.

Learning Pixel-Level Distinctions for Video Highlight Detection

no code implementations10 Apr 2022 Fanyue Wei, Biao Wang, Tiezheng Ge, Yuning Jiang, Wen Li, Lixin Duan

To this end, we propose to learn pixel-level distinctions to improve the video highlight detection.

Frame Highlight Detection

Semantic-Aware Domain Generalized Segmentation

1 code implementation2 Apr 2022 Duo Peng, Yinjie Lei, Munawar Hayat, Yulan Guo, Wen Li

In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data.

Semantic Segmentation

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

no code implementations1 Apr 2022 Yan Zhang, Changyu Li, Ivor W. Tsang, Hui Xu, Lixin Duan, Hongzhi Yin, Wen Li, Jie Shao

Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues.

Domain Adaptation Meta-Learning +1

Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound

no code implementations13 Mar 2022 Feiyu Wang, Qin Wang, Wen Li, Dong Xu, Luc van Gool

Benefited from this new perspective, we first propose a new deep semi-supervised learning framework called Semi-supervised Learning by Empirical Distribution Alignment (SLEDA), in which existing technologies from the domain adaptation community can be readily used to address the semi-supervised learning problem through reducing the empirical distribution distance between labeled and unlabeled data.

Data Augmentation Domain Adaptation

Move As You Like: Image Animation in E-Commerce Scenario

no code implementations19 Dec 2021 Borun Xu, Biao Wang, Jiale Tao, Tiezheng Ge, Yuning Jiang, Wen Li, Lixin Duan

Creative image animations are attractive in e-commerce applications, where motion transfer is one of the import ways to generate animations from static images.

Image Animation

HBReID: Harder Batch for Re-identification

no code implementations9 Dec 2021 Wen Li, Furong Xu, Jianan Zhao, Ruobing Zheng, Cheng Zou, Meng Wang, Yuan Cheng

Triplet loss is a widely adopted loss function in ReID task which pulls the hardest positive pairs close and pushes the hardest negative pairs far away.

Person Re-Identification

LZ1904 at SemEval-2021 Task 5: Bi-LSTM-CRF for Toxic Span Detection using Pretrained Word Embedding

no code implementations SEMEVAL 2021 Liang Zou, Wen Li

And then we construct Bidirectional Long Short Term Memory-Conditional Random Field (Bi-LSTM-CRF) model by Baidu research to predict whether each word in the sentence is toxic or not.

Machine Translation Text Classification +1

SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object Detection

no code implementations CVPR 2021 Weichen Zhang, Wen Li, Dong Xu

In this work, we propose a new cross-dataset 3D object detection method named Scale-aware and Range-aware Domain Adaptation Network (SRDAN).

3D Object Detection Domain Adaptation

VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation

no code implementations26 Mar 2021 Jiayi Tian, Jing Zhang, Wen Li, Dong Xu

On the other hand, we also design an effective distribution alignment method to reduce the distribution divergence between the virtual domain and the target domain by gradually improving the compactness of the target domain distribution through model learning.

Object Recognition Unsupervised Domain Adaptation

BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-Domain Semantic Segmentation

1 code implementation ICCV 2021 Yahao Liu, Jinhong Deng, Xinchen Gao, Wen Li, Lixin Duan

By integrating the boundary adaptation and prototype alignment, we are able to train a discriminative and domain-invariant model for cross-domain semantic segmentation.

Semantic Segmentation Unsupervised Domain Adaptation

Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation

no code implementations CVPR 2021 Rui Gong, Yuhua Chen, Danda Pani Paudel, Yawei Li, Ajad Chhatkuli, Wen Li, Dengxin Dai, Luc van Gool

Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.

Domain Adaptation Meta-Learning +2

Region Comparison Network for Interpretable Few-shot Image Classification

1 code implementation8 Sep 2020 Zhiyu Xue, Lixin Duan, Wen Li, Lin Chen, Jiebo Luo

For that, in this work, we propose a metric learning based method named Region Comparison Network (RCN), which is able to reveal how few-shot learning works as in a neural network as well as to find out specific regions that are related to each other in images coming from the query and support sets.

Classification Few-Shot Image Classification +2

Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval

no code implementations7 Aug 2020 Tao Wu, Ellie Ka-In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao

In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.

Information Retrieval Recommendation Systems +1

Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

1 code implementation ECCV 2020 Yuan Tian, Qin Wang, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.

Image Generation Neural Architecture Search +1

The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures

1 code implementation CVPR 2021 Yawei Li, Wen Li, Martin Danelljan, Kai Zhang, Shuhang Gu, Luc van Gool, Radu Timofte

Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.

Image Classification Image Restoration +1

Analogical Image Translation for Fog Generation

no code implementations28 Jun 2020 Rui Gong, Dengxin Dai, Yu-Hua Chen, Wen Li, Luc van Gool

AIT achieves this zero-shot image translation capability by coupling a supervised training scheme in the synthetic domain, a cycle consistency strategy in the real domain, an adversarial training scheme between the two domains, and a novel network design.

Image-to-Image Translation Scene Understanding +1

Deeply Aligned Adaptation for Cross-domain Object Detection

no code implementations5 Apr 2020 Minghao Fu, Zhenshan Xie, Wen Li, Lixin Duan

Cross-domain object detection has recently attracted more and more attention for real-world applications, since it helps build robust detectors adapting well to new environments.

Object Detection

Unbiased Mean Teacher for Cross-domain Object Detection

1 code implementation CVPR 2021 Jinhong Deng, Wen Li, Yu-Hua Chen, Lixin Duan

We reveal that there often exists a considerable model bias for the simple mean teacher (MT) model in cross-domain scenarios, and eliminate the model bias with several simple yet highly effective strategies.

Object Detection Small Data Image Classification

Ensemble Methods to Distinguish Mainland and Taiwan Chinese

no code implementations WS 2019 Hai Hu, Wen Li, He Zhou, Zuoyu Tian, Yiwen Zhang, Liang Zou

This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese.

Word Embeddings

NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers

no code implementations SEMEVAL 2019 Ping Liu, Wen Li, Liang Zou

Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e. g., image recognition) and natural language processing (e. g., text classification).

General Classification Language Identification +2

Semi-Supervised Learning by Augmented Distribution Alignment

1 code implementation ICCV 2019 Qin Wang, Wen Li, Luc van Gool

We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data.

Domain Adaptation Semi-Supervised Image Classification

Known-class Aware Self-ensemble for Open Set Domain Adaptation

1 code implementation3 May 2019 Qing Lian, Wen Li, Lin Chen, Lixin Duan

Particularly, in open set domain adaptation, we allow the classes from the source and target domains to be partially overlapped.

Domain Adaptation

Sliced Wasserstein Generative Models

1 code implementation CVPR 2019 Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.

Image Generation Video Generation

DLOW: Domain Flow for Adaptation and Generalization

1 code implementation CVPR 2019 Rui Gong, Wen Li, Yu-Hua Chen, Luc van Gool

In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other.

Domain Adaptation Semantic Segmentation +1

Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach

no code implementations CVPR 2019 Yuhua Chen, Wen Li, Xiaoran Chen, Luc van Gool

In this work, we take the advantage of additional geometric information from synthetic data, a powerful yet largely neglected cue, to bridge the domain gap.

Depth Estimation Semantic Segmentation

Dividing and Aggregating Network for Multi-view Action Recognition

no code implementations ECCV 2018 Dongang Wang, Wanli Ouyang, Wen Li, Dong Xu

We then train view-specific action classifiers based on the view-specific representation for each view and a view classifier based on the shared representation at lower layers.

Action Recognition

Collaborative and Adversarial Network for Unsupervised Domain Adaptation

1 code implementation CVPR 2018 Weichen Zhang, Wanli Ouyang, Wen Li, Dong Xu

In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN) through domain-collaborative and domain-adversarial training of neural networks.

Unsupervised Domain Adaptation

Detecting Syntactic Features of Translated Chinese

no code implementations WS 2018 Hai Hu, Wen Li, Sandra Kübler

We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features.


Appearance-and-Relation Networks for Video Classification

1 code implementation CVPR 2018 Limin Wang, Wei Li, Wen Li, Luc van Gool

Specifically, SMART blocks decouple the spatiotemporal learning module into an appearance branch for spatial modeling and a relation branch for temporal modeling.

Action Classification Action Recognition +3

Gender Prediction for Chinese Social Media Data

no code implementations RANLP 2017 Wen Li, Markus Dickinson

The goal of this project is to predict the gender of users based on their posts on Weibo, a Chinese micro-blogging platform.

Chinese Word Segmentation Gender Prediction +2

WebVision Database: Visual Learning and Understanding from Web Data

no code implementations9 Aug 2017 Wen Li, Li-Min Wang, Wei Li, Eirikur Agustsson, Luc van Gool

Our new WebVision database and relevant studies in this work would benefit the advance of learning state-of-the-art visual models with minimum supervision based on web data.

Domain Adaptation

Deep Domain Adaptation by Geodesic Distance Minimization

no code implementations13 Jul 2017 Yifei Wang, Wen Li, Dengxin Dai, Luc van Gool

Our work builds on the recently proposed Deep CORAL method, which proposed to train a convolutional neural network and simultaneously minimize the Euclidean distance of convariance matrices between the source and target domains.

Domain Adaptation

Sliced Wasserstein Generative Models

1 code implementation8 Jun 2017 Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.

Image Generation Video Generation

WebVision Challenge: Visual Learning and Understanding With Web Data

no code implementations16 May 2017 Wen Li, Li-Min Wang, Wei Li, Eirikur Agustsson, Jesse Berent, Abhinav Gupta, Rahul Sukthankar, Luc van Gool

The 2017 WebVision challenge consists of two tracks, the image classification task on WebVision test set, and the transfer learning task on PASCAL VOC 2012 dataset.

Image Classification Transfer Learning

Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)

3 code implementations8 Oct 2016 Wen Li, Ying Zhang, Yifang Sun, Wei Wang, Wenjie Zhang, Xuemin Lin

Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision.


Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

2 code implementations12 Jul 2016 Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, Wen Li

In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition.

Classification General Classification +2

Fast Algorithms for Linear and Kernel SVM+

no code implementations CVPR 2016 Wen Li, Dengxin Dai, Mingkui Tan, Dong Xu, Luc van Gool

The SVM+ approach has shown excellent performance in visual recognition tasks for exploiting privileged information in the training data.

Multi-View Domain Generalization for Visual Recognition

no code implementations ICCV 2015 Li Niu, Wen Li, Dong Xu

Considering the recent works show the domain generalization capability can be enhanced by fusing multiple SVM classifiers, we build upon exemplar SVMs to learn a set of SVM classifiers by using one positive sample and all negative samples in the source domain each time.

Domain Generalization

Visual Recognition by Learning From Web Data: A Weakly Supervised Domain Generalization Approach

no code implementations CVPR 2015 Li Niu, Wen Li, Dong Xu

In this work, we formulate a new weakly supervised domain generalization problem for the visual recognition task by using loosely labeled web images/videos as training data.

Domain Generalization

FaLRR: A Fast Low Rank Representation Solver

no code implementations CVPR 2015 Shijie Xiao, Wen Li, Dong Xu, DaCheng Tao

In this paper, we develop a fast LRR solver called FaLRR, by reformulating LRR as a new optimization problem with regard to factorized data (which is obtained by skinny SVD of the original data matrix).

Face Clustering

Recognizing RGB Images by Learning from RGB-D Data

no code implementations CVPR 2014 Lin Chen, Wen Li, Dong Xu

In this work, we propose a new framework for recognizing RGB images captured by the conventional cameras by leveraging a set of labeled RGB-D data, in which the depth features can be additionally extracted from the depth images.

Object Recognition Unsupervised Domain Adaptation

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