Search Results for author: Xinchao Wang

Found 69 papers, 27 papers with code

Collaboration by Competition: Self-coordinated Knowledge Amalgamation for Multi-talent Student Learning

no code implementations ECCV 2020 Sihui Luo, Wenwen Pan, Xinchao Wang, Dazhou Wang, Haihong Tang, Mingli Song

To this end, we propose a self-coordinate knowledge amalgamation network (SOKA-Net) for learning the multi-talent student model.

Hallucinating Visual Instances in Total Absentia

no code implementations ECCV 2020 Jiayan Qiu, Yiding Yang, Xinchao Wang, DaCheng Tao

This seemingly minor difference in fact makes the HVITA a much challenging task, as the restoration algorithm would have to not only infer the category of the object in total absentia, but also hallucinate an object of which the appearance is consistent with the background.

Image Inpainting

MetaFormer is Actually What You Need for Vision

2 code implementations22 Nov 2021 Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan

Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance.

Image Classification Semantic Segmentation

Meta Clustering Learning for Large-scale Unsupervised Person Re-identification

no code implementations19 Nov 2021 Xin Jin, Tianyu He, Zhiheng Yin, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Xian-Sheng Hua, Zhibo Chen

Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms.

Unsupervised Person Re-Identification

MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition

no code implementations27 Oct 2021 Jinming Zhao, Ruichen Li, Qin Jin, Xinchao Wang, Haizhou Li

Multimodal emotion recognition study is hindered by the lack of labelled corpora in terms of scale and diversity, due to the high annotation cost and label ambiguity.

Emotion Classification Multimodal Emotion Recognition +1

Unleash the Potential of Adaptation Models via Dynamic Domain Labels

no code implementations29 Sep 2021 Xin Jin, Tianyu He, Xu Shen, Songhua Wu, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua

In this paper, we propose an embarrassing simple yet highly effective adversarial domain adaptation (ADA) method for effectively training models for alignment.

Domain Adaptation

How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?

no code implementations NeurIPS Workshop ImageNet_PPF 2021 Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.

Self-Supervised Learning

Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks

no code implementations ICCV 2021 Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao

In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs).

Structure-Aware Feature Generation for Zero-Shot Learning

no code implementations16 Aug 2021 Lianbo Zhang, Shaoli Huang, Xinchao Wang, Wei Liu, DaCheng Tao

In this paper, we introduce a novel structure-aware feature generation scheme, termed as SA-GAN, to explicitly account for the topological structure in learning both the latent space and the generative networks.

Zero-Shot Learning

Edge-competing Pathological Liver Vessel Segmentation with Limited Labels

1 code implementation1 Aug 2021 Zunlei Feng, Zhonghua Wang, Xinchao Wang, Xiuming Zhang, Lechao Cheng, Jie Lei, Yuexuan Wang, Mingli Song

The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming.

whole slide images

Boundary Knowledge Translation based Reference Semantic Segmentation

no code implementations1 Aug 2021 Lechao Cheng, Zunlei Feng, Xinchao Wang, Ya Jie Liu, Jie Lei, Mingli Song

In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation.

Semantic Segmentation Translation

Visual Boundary Knowledge Translation for Foreground Segmentation

no code implementations1 Aug 2021 Zunlei Feng, Lechao Cheng, Xinchao Wang, Xiang Wang, Yajie Liu, Xiangtong Du, Mingli Song

To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.

Semantic Segmentation Translation

Tree-Like Decision Distillation

no code implementations CVPR 2021 Jie Song, Haofei Zhang, Xinchao Wang, Mengqi Xue, Ying Chen, Li Sun, DaCheng Tao, Mingli Song

Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model.

Decision Making Knowledge Distillation

Scene Essence

no code implementations CVPR 2021 Jiayan Qiu, Yiding Yang, Xinchao Wang, DaCheng Tao

What scene elements, if any, are indispensable for recognizing a scene?

Scene Recognition

Turning Frequency to Resolution: Video Super-Resolution via Event Cameras

no code implementations CVPR 2021 Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao

To this end, we propose an Event-based VSR framework (E-VSR), of which the key component is an asynchronous interpolation (EAI) module that reconstructs a high-frequency (HF) video stream with uniform and tiny pixel displacements between neighboring frames from an event stream.

Video Super-Resolution

Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking

no code implementations CVPR 2021 Yiding Yang, Zhou Ren, Haoxiang Li, Chunluan Zhou, Xinchao Wang, Gang Hua

In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion.

Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1

Contrastive Model Inversion for Data-Free Knowledge Distillation

1 code implementation18 May 2021 Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, Mingli Song

In this paper, we propose Contrastive Model Inversion~(CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue.

Contrastive Learning Knowledge Distillation

Online Multiple Object Tracking with Cross-Task Synergy

1 code implementation CVPR 2021 Song Guo, Jingya Wang, Xinchao Wang, DaCheng Tao

On the other hand, such reliable embeddings can boost identity-awareness through memory aggregation, hence strengthen attention modules and suppress drifts.

Multiple Object Tracking

Training Generative Adversarial Networks in One Stage

1 code implementation CVPR 2021 Chengchao Shen, Youtan Yin, Xinchao Wang, Xubin Li, Jie Song, Mingli Song

Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort.

Image Generation Knowledge Distillation

SPAGAN: Shortest Path Graph Attention Network

1 code implementation10 Jan 2021 Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, DaCheng Tao

SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further {a} more effective aggregation of information from distant neighbors into the center node, as compared to node-based GCN methods.

Graph Attention

Self-Born Wiring for Neural Trees

no code implementations ICCV 2021 Ying Chen, Feng Mao, Jie Song, Xinchao Wang, Huiqiong Wang, Mingli Song

Neural trees aim at integrating deep neural networks and decision trees so as to bring the best of the two worlds, including representation learning from the former and faster inference from the latter.

Representation Learning

Stochastic Partial Swap: Enhanced Model Generalization and Interpretability for Fine-Grained Recognition

no code implementations ICCV 2021 Shaoli Huang, Xinchao Wang, DaCheng Tao

Learning mid-level representation for fine-grained recognition is easily dominated by a limited number of highly discriminative patterns, degrading its robustness and generalization capability.

Material Recognition Scene Recognition

Overcoming Catastrophic Forgetting in Graph Neural Networks

1 code implementation10 Dec 2020 Huihui Liu, Yiding Yang, Xinchao Wang

Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks.

Continual Learning

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

2 code implementations9 Dec 2020 Shaoli Huang, Xinchao Wang, DaCheng Tao

As the main discriminative information of a fine-grained image usually resides in subtle regions, methods along this line are prone to heavy label noise in fine-grained recognition.

Fine-Grained Image Classification Semantic Composition +1

Progressive Network Grafting for Few-Shot Knowledge Distillation

1 code implementation9 Dec 2020 Chengchao Shen, Xinchao Wang, Youtan Yin, Jie Song, Sihui Luo, Mingli Song

In this paper, we investigate the practical few-shot knowledge distillation scenario, where we assume only a few samples without human annotations are available for each category.

Knowledge Distillation Model Compression +1

One-sample Guided Object Representation Disassembling

no code implementations NeurIPS 2020 Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei, Cheng Jin, Mingli Song

In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.

Data Augmentation Image Classification

Learning Propagation Rules for Attribution Map Generation

no code implementations ECCV 2020 Yiding Yang, Jiayan Qiu, Mingli Song, DaCheng Tao, Xinchao Wang

Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the input and then the attribution map.

Factorizable Graph Convolutional Networks

1 code implementation NeurIPS 2020 Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang

In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph.

Graph Classification Graph Regression +1

Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets

no code implementations18 Jul 2020 Weihong Ren, Xinchao Wang, Jiandong Tian, Yandong Tang, Antoni B. Chan

State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors.

Multi-Object Tracking

Impression Space from Deep Template Network

no code implementations10 Jul 2020 Gongfan Fang, Xinchao Wang, Haofei Zhang, Jie Song, Mingli Song

This network is referred to as the {\emph{Template Network}} because its filters will be used as templates to reconstruct images from the impression.

Image Generation Translation

Disassembling Object Representations without Labels

no code implementations3 Apr 2020 Zunlei Feng, Xinchao Wang, Yongming He, Yike Yuan, Xin Gao, Mingli Song

In this paper, we study a new representation-learning task, which we termed as disassembling object representations.

General Classification Representation Learning +1

Learning Oracle Attention for High-fidelity Face Completion

no code implementations CVPR 2020 Tong Zhou, Changxing Ding, Shaowen Lin, Xinchao Wang, DaCheng Tao

While recent works adopted the attention mechanism to learn the contextual relations among elements of the face, they have largely overlooked the disastrous impacts of inaccurate attention scores; in addition, they fail to pay sufficient attention to key facial components, the completion results of which largely determine the authenticity of a face image.

Facial Inpainting

Distilling Knowledge from Graph Convolutional Networks

1 code implementation CVPR 2020 Yiding Yang, Jiayan Qiu, Mingli Song, DaCheng Tao, Xinchao Wang

To enable the knowledge transfer from the teacher GCN to the student, we propose a local structure preserving module that explicitly accounts for the topological semantics of the teacher.

Knowledge Distillation Transfer Learning

DEPARA: Deep Attribution Graph for Deep Knowledge Transferability

1 code implementation CVPR 2020 Jie Song, Yixin Chen, Jingwen Ye, Xinchao Wang, Chengchao Shen, Feng Mao, Mingli Song

In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs.

Model Selection Transfer Learning

Data-Free Adversarial Distillation

2 code implementations23 Dec 2019 Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, Mingli Song

Knowledge Distillation (KD) has made remarkable progress in the last few years and become a popular paradigm for model compression and knowledge transfer.

Knowledge Distillation Model Compression +2

Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers

no code implementations26 Nov 2019 Ya Zhao, Rui Xu, Xinchao Wang, Peng Hou, Haihong Tang, Mingli Song

In this paper, we propose a new method, termed as Lip by Speech (LIBS), of which the goal is to strengthen lip reading by learning from speech recognizers.

 Ranked #1 on Lipreading on CMLR

Knowledge Distillation Lipreading +2

Dynamic Instance Normalization for Arbitrary Style Transfer

no code implementations16 Nov 2019 Yongcheng Jing, Xiao Liu, Yukang Ding, Xinchao Wang, Errui Ding, Mingli Song, Shilei Wen

Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way.

Style Transfer

Deep Model Transferability from Attribution Maps

1 code implementation NeurIPS 2019 Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song

Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so as to reduce the training effort of the latter.

Transfer Learning

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation

1 code implementation ICCV 2019 Chengchao Shen, Mengqi Xue, Xinchao Wang, Jie Song, Li Sun, Mingli Song

To this end, we introduce a dual-step strategy that first extracts the task-specific knowledge from the heterogeneous teachers sharing the same sub-task, and then amalgamates the extracted knowledge to build the student network.

Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning

2 code implementations24 Jun 2019 Sihui Luo, Xinchao Wang, Gongfan Fang, Yao Hu, Dapeng Tao, Mingli Song

An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without accessing the training annotations.

One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation

1 code implementation5 Jun 2019 Chenhong Zhou, Changxing Ding, Xinchao Wang, Zhentai Lu, DaCheng Tao

The model cascade (MC) strategy significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation.

Brain Tumor Segmentation Curriculum Learning +1

Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers

1 code implementation28 May 2019 Jingwen Ye, Xinchao Wang, Yixin Ji, Kairi Ou, Mingli Song

Many well-trained Convolutional Neural Network(CNN) models have now been released online by developers for the sake of effortless reproducing.

Not All Parts Are Created Equal: 3D Pose Estimation by Modelling Bi-directional Dependencies of Body Parts

no code implementations20 May 2019 Jue Wang, Shaoli Huang, Xinchao Wang, DaCheng Tao

We model parts with higher DOFs like the elbows, as dependent components of the corresponding parts with lower DOFs like the torso, of which the 3D locations can be more reliably estimated.

3D Pose Estimation

Amalgamating Knowledge towards Comprehensive Classification

1 code implementation7 Nov 2018 Chengchao Shen, Xinchao Wang, Jie Song, Li Sun, Mingli Song

We propose in this paper to study a new model-reusing task, which we term as \emph{knowledge amalgamation}.

Classification General Classification

Geometry-Aware Scene Text Detection With Instance Transformation Network

no code implementations CVPR 2018 Fangfang Wang, Liming Zhao, Xi Li, Xinchao Wang, DaCheng Tao

Localizing text in the wild is challenging in the situations of complicated geometric layout of the targets like random orientation and large aspect ratio.

General Classification Multi-Task Learning +3

Dual Swap Disentangling

1 code implementation NeurIPS 2018 Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, DaCheng Tao, Mingli Song

To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs.

Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks

no code implementations22 Apr 2018 Fusheng Hao, Jun Cheng, Lei Wang, Xinchao Wang, Jianzhong Cao, Xiping Hu, Dapeng Tao

Discriminative features are obtained by constraining the deep CNNs to map training samples to the corresponding anchors as close as possible.

Image Classification

Horizontal Pyramid Matching for Person Re-identification

1 code implementation14 Apr 2018 Yang Fu, Yunchao Wei, Yuqian Zhou, Honghui Shi, Gao Huang, Xinchao Wang, Zhiqiang Yao, Thomas Huang

Despite the remarkable recent progress, person re-identification (Re-ID) approaches are still suffering from the failure cases where the discriminative body parts are missing.

Person Re-Identification

Deep Motion Boundary Detection

no code implementations13 Apr 2018 Xiaoqing Yin, Xiyang Dai, Xinchao Wang, Maojun Zhang, DaCheng Tao, Larry Davis

In this paper, we propose the first dedicated end-to-end deep learning approach for motion boundary detection, which we term as MoBoNet.

Boundary Detection Optical Flow Estimation

Improving Object Detection from Scratch via Gated Feature Reuse

2 code implementations4 Dec 2017 Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas Huang, Marios Savvides

In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i. e., without pre-trained models).

Object Detection

Non-Markovian Globally Consistent Multi-Object Tracking

no code implementations ICCV 2017 Andrii Maksai, Xinchao Wang, Francois Fleuret, Pascal Fua

Many state-of-the-art approaches to multi-object tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories.

Multi-Object Tracking

On Compressing Deep Models by Low Rank and Sparse Decomposition

no code implementations CVPR 2017 Xiyu Yu, Tongliang Liu, Xinchao Wang, DaCheng Tao

Deep compression refers to removing the redundancy of parameters and feature maps for deep learning models.

Globally Consistent Multi-People Tracking using Motion Patterns

1 code implementation2 Dec 2016 Andrii Maksai, Xinchao Wang, Francois Fleuret, Pascal Fua

Many state-of-the-art approaches to people tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories.

Do We Need Binary Features for 3D Reconstruction?

no code implementations14 Feb 2016 Bin Fan, Qingqun Kong, Wei Sui, Zhiheng Wang, Xinchao Wang, Shiming Xiang, Chunhong Pan, Pascal Fua

Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors.

3D Reconstruction

Predicting People's 3D Poses from Short Sequences

no code implementations30 Apr 2015 Bugra Tekin, Xiaolu Sun, Xinchao Wang, Vincent Lepetit, Pascal Fua

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people.

Globally Optimal Cell Tracking using Integer Programming

no code implementations22 Jan 2015 Engin Türetken, Xinchao Wang, Carlos Becker, Carsten Haubold, Pascal Fua

We propose a novel approach to automatically tracking cell populations in time-lapse images.

Multiple human pose estimation with temporally consistent 3d pictorial structures

no code implementations6 Sep 2014 Vasileios Belagiannis, Xinchao Wang, Bernt Schiele, Pascal Fua, Slobodan Ilic, Nassir Navab

To address these challenges, we propose a temporally consistent 3D Pictorial Structures model (3DPS) for multiple human pose estimation from multiple camera views.

3D Multi-Person Pose Estimation 3D Pose Estimation

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