Search Results for author: Wei Shen

Found 57 papers, 19 papers with code

Inductive Matrix Completion Using Graph Autoencoder

1 code implementation25 Aug 2021 Wei Shen, Chuheng Zhang, Yun Tian, Liang Zeng, Xiaonan He, Wanchun Dou, Xiaolong Xu

However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).

Matrix Completion

An Efficient Group-based Search Engine Marketing System for E-Commerce

no code implementations24 Jun 2021 Cheng Jie, Da Xu, Zigeng Wang, Lu Wang, Wei Shen

With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies.

Dual Attention Guided Gaze Target Detection in the Wild

1 code implementation CVPR 2021 Yi Fang, Jiapeng Tang, Wang Shen, Wei Shen, Xiao Gu, Li Song, Guangtao Zhai

In the third stage, we use the generated dual attention as guidance to perform two sub-tasks: (1) identifying whether the gaze target is inside or out of the image; (2) locating the target if inside.

Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning

no code implementations5 Jun 2021 Yilin Wang, Shaozuo Yu, Xiaokang Yang, Wei Shen

In this paper, we propose a generic model transfer scheme to make Convlutional Neural Networks (CNNs) interpretable, while maintaining their high classification accuracy.


Glance-and-Gaze Vision Transformer

1 code implementation4 Jun 2021 Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan Yuille, Wei Shen

It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context.

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

no code implementations31 May 2021 Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.

Multiple Instance Learning

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

1 code implementation5 Mar 2021 Boxiang Yun, Yan Wang, Jieneng Chen, Huiyu Wang, Wei Shen, Qingli Li

Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine.

Semantic Segmentation

Batch Normalization with Enhanced Linear Transformation

1 code implementation28 Nov 2020 Yuhui Xu, Lingxi Xie, Cihang Xie, Jieru Mei, Siyuan Qiao, Wei Shen, Hongkai Xiong, Alan Yuille

Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions.

Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples

no code implementations29 Oct 2020 Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.

Pancreas Segmentation Volumetric Medical Image Segmentation

Shape-Texture Debiased Neural Network Training

1 code implementation ICLR 2021 Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie

To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.

Data Augmentation Image Classification +1

CO2: Consistent Contrast for Unsupervised Visual Representation Learning

no code implementations ICLR 2021 Chen Wei, Huiyu Wang, Wei Shen, Alan Yuille

Regarding the similarity of the query crop to each crop from other images as "unlabeled", the consistency term takes the corresponding similarity of a positive crop as a pseudo label, and encourages consistency between these two similarities.

Contrastive Learning Image Classification +3

Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing

no code implementations25 Aug 2020 Wei Shen, Xiaonan He, Chuheng Zhang, Qiang Ni, Wanchun Dou, Yan Wang

Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals.

Combinatorial Optimization Fairness

Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks

no code implementations9 Jul 2020 Daniil Pakhomov, Wei Shen, Nassir Navab

Surgical tool segmentation in endoscopic images is an important problem: it is a crucial step towards full instrument pose estimation and it is used for integration of pre- and intra-operative images into the endoscopic view.

Image-to-Image Translation Pose Estimation +1

Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation

no code implementations18 May 2020 Shuhao Fu, Yongyi Lu, Yan Wang, Yuyin Zhou, Wei Shen, Elliot Fishman, Alan Yuille

In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).

Relational Reasoning Super-Resolution +1

Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors

no code implementations4 Apr 2020 Zhuotun Zhu, Yongyi Lu, Wei Shen, Elliot K. Fishman, Alan L. Yuille

This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans.

Classification General Classification

Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

1 code implementation ECCV 2020 Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, Alan Yuille

The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis.

Anomaly Detection Autonomous Driving +2

Deep Distance Transform for Tubular Structure Segmentation in CT Scans

no code implementations CVPR 2020 Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.

Deeply Shape-guided Cascade for Instance Segmentation

1 code implementation CVPR 2021 Hao Ding, Siyuan Qiao, Alan Yuille, Wei Shen

The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across multiple stages.

Instance Segmentation Region Proposal +1

Rethinking Normalization and Elimination Singularity in Neural Networks

1 code implementation21 Nov 2019 Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille

To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models.

Image Classification Instance Segmentation +2

TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion

no code implementations9 Sep 2019 Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, Alan Yuille

Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.

General Classification Object Classification +1

Deep Differentiable Random Forests for Age Estimation

no code implementations23 Jul 2019 Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo wang, Alan Yuille

Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes.

Age Estimation

Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks

no code implementations30 Jun 2019 Wei Shen, Fei Li, Rujie Liu

We argue that the discard of the correlated discriminative information is partially caused by the fact that the minimization of the classification loss doesn't ensure to learn the overall discriminative information but only the most discriminative information.

Classification General Classification +1

Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning

no code implementations25 Apr 2019 Wei Shen, Zhenhuan Yang, Yiming Ying, Xiaoming Yuan

From this fundamental trade-off, we obtain lower bounds for the optimization error of SGD algorithms and the excess expected risk over a class of pairwise losses.

Generalization Bounds Metric Learning

Learning from Adversarial Features for Few-Shot Classification

no code implementations25 Mar 2019 Wei Shen, Ziqiang Shi, Jun Sun

Then we use the adversarial region attention to aggregate the feature maps to obtain the adversarial features.

Classification Few-Shot Learning +1

Learning to generate filters for convolutional neural networks

no code implementations ICLR 2018 Wei Shen, Rujie Liu

In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass.

Robust Face Detection via Learning Small Faces on Hard Images

1 code implementation28 Nov 2018 Zhishuai Zhang, Wei Shen, Siyuan Qiao, Yan Wang, Bo wang, Alan Yuille

In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images.

Face Detection

Generating Attention from Classifier Activations for Fine-grained Recognition

no code implementations27 Nov 2018 Wei Shen, Rujie Liu

Recent advances in fine-grained recognition utilize attention maps to localize objects of interest.

Semantic Segmentation

Tackling Early Sparse Gradients in Softmax Activation Using Leaky Squared Euclidean Distance

no code implementations27 Nov 2018 Wei Shen, Rujie Liu

However, we find that choosing squared Euclidean distance may cause distance explosion leading gradients to be extremely sparse in the early stage of back propagation.

One-Shot Learning

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

3 code implementations9 Jul 2018 Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Yuille

The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.

Multiple Instance Learning Object Recognition +1

Hifi: Hierarchical feature integration for skeleton detection

no code implementations1 Jul 2018 Kai Zhao, Wei Shen, ShangHua Gao, Dandan Li, Ming-Ming Cheng

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts.

Object Skeleton Detection

Resisting Large Data Variations via Introspective Transformation Network

no code implementations16 May 2018 Yunhan Zhao, Ye Tian, Charless Fowlkes, Wei Shen, Alan Yuille

Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.

Data Augmentation Few-Shot Learning

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

no code implementations23 Apr 2018 Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K. Fishman, Alan L. Yuille

To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.

Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound

no code implementations7 Apr 2018 Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).

Medical Image Segmentation

Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

no code implementations7 Apr 2018 Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot K. Fishman, Alan L. Yuille

In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.

Semantic Segmentation

Deep Co-Training for Semi-Supervised Image Recognition

1 code implementation ECCV 2018 Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo wang, Alan Yuille

We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework.

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

no code implementations5 Jan 2018 Kai Zhao, Wei Shen, Shang-Hua Gao, Dandan Li, Ming-Ming Cheng

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem.

Object Skeleton Detection

Deep Regression Forests for Age Estimation

2 code implementations CVPR 2018 Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo wang, Alan Yuille

Age estimation from facial images is typically cast as a nonlinear regression problem.

Age Estimation

Single-Shot Object Detection with Enriched Semantics

no code implementations CVPR 2018 Zhishuai Zhang, Siyuan Qiao, Cihang Xie, Wei Shen, Bo wang, Alan L. Yuille

Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.

Object Detection Semantic Segmentation

Gradually Updated Neural Networks for Large-Scale Image Recognition

no code implementations ICML 2018 Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo wang, Alan Yuille

Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner.

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

no code implementations ICCV 2017 Siyuan Qiao, Wei Shen, Weichao Qiu, Chenxi Liu, Alan Yuille

We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.

Object Proposal Generation

Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

no code implementations ICCV 2017 Wei Shen, Bin Wang, Yuan Jiang, Yan Wang, Alan Yuille

This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue.

Boundary Detection Electron Microscopy

Label Distribution Learning Forests

no code implementations NeurIPS 2017 Wei Shen, Kai Zhao, Yilu Guo, Alan Yuille

This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions.

Representation Learning

A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

3 code implementations25 Dec 2016 Yuyin Zhou, Lingxi Xie, Wei Shen, Yan Wang, Elliot K. Fishman, Alan L. Yuille

Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.

Pancreas Segmentation

Learning Residual Images for Face Attribute Manipulation

1 code implementation CVPR 2017 Wei Shen, Rujie Liu

The transformation networks are responsible for the attribute manipulation and its dual operation and the discriminative network is used to distinguish the generated images from real images.

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

1 code implementation13 Sep 2016 Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Xiang Bai, Alan Yuille

By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network.

Multi-Task Learning Object Detection +1

Shape Recognition by Bag of Skeleton-associated Contour Parts

no code implementations20 May 2016 Wei Shen, Yuan Jiang, Wenjing Gao, Dan Zeng, Xinggang Wang

Contour and skeleton are two complementary representations for shape recognition.

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

no code implementations CVPR 2016 Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Zhijiang Zhang, Xiang Bai

Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts.

Object Detection

Symmetry-Based Text Line Detection in Natural Scenes

no code implementations CVPR 2015 Zheng Zhang, Wei Shen, Cong Yao, Xiang Bai

Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes.

Scene Text Scene Text Detection

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