Search Results for author: Lingxi Xie

Found 98 papers, 42 papers with code

SIMILE: Introducing Sequential Information towards More Effective Imitation Learning

no code implementations ICLR 2019 Yutong Bai, Lingxi Xie

Reinforcement learning (RL) is a metaheuristic aiming at teaching an agent to interact with an environment and maximizing the reward in a complex task.

Imitation Learning OpenAI Gym

Exploring Complicated Search Spaces with Interleaving-Free Sampling

no code implementations5 Dec 2021 Yunjie Tian, Lingxi Xie, Jiemin Fang, Jianbin Jiao, Qixiang Ye, Qi Tian

In this paper, we build the search algorithm upon a complicated search space with long-distance connections, and show that existing weight-sharing search algorithms mostly fail due to the existence of \textbf{interleaved connections}.

Neural Architecture Search

NeuSample: Neural Sample Field for Efficient View Synthesis

no code implementations30 Nov 2021 Jiemin Fang, Lingxi Xie, Xinggang Wang, Xiaopeng Zhang, Wenyu Liu, Qi Tian

Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy.

Semantic-Aware Generation for Self-Supervised Visual Representation Learning

1 code implementation25 Nov 2021 Yunjie Tian, Lingxi Xie, Xiaopeng Zhang, Jiemin Fang, Haohang Xu, Wei Huang, Jianbin Jiao, Qi Tian, Qixiang Ye

In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.

Representation Learning

Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes

no code implementations24 Nov 2021 Jiazhong Cen, Zenkun Jiang, Lingxi Xie, Qi Tian, Xiaokang Yang, Wei Shen

Anomaly segmentation is a crucial task for safety-critical applications, such as autonomous driving in urban scenes, where the goal is to detect out-of-distribution (OOD) objects with categories which are unseen during training.

Anomaly Detection Autonomous Driving

CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis

1 code implementation19 Oct 2021 Peng Zhou, Lingxi Xie, Bingbing Ni, Qi Tian

The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses.

3D-Aware Image Synthesis Transfer Learning

Vibration-based Uncertainty Estimation for Learning from Limited Supervision

no code implementations29 Sep 2021 Hengtong Hu, Lingxi Xie, Yinquan Wang, Richang Hong, Meng Wang, Qi Tian

We investigate the problem of estimating uncertainty for training data, so that deep neural networks can make use of the results for learning from limited supervision.

Active Learning

Deep Encryption: Protecting Pre-Trained Neural Networks with Confusion Neurons

no code implementations29 Sep 2021 Mengbiao Zhao, Shixiong Xu, Jianlong Chang, Lingxi Xie, Jie Chen, Qi Tian

Having consumed huge amounts of training data and computational resource, large-scale pre-trained models are often considered key assets of AI service providers.

Bag of Instances Aggregation Boosts Self-supervised Learning

1 code implementation4 Jul 2021 Haohang Xu, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, Qi Tian

Recent advances in self-supervised learning have experienced remarkable progress, especially for contrastive learning based methods, which regard each image as well as its augmentations as an individual class and try to distinguish them from all other images.

Contrastive Learning Self-Supervised Learning

ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation

no code implementations CVPR 2021 Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian

Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data.

Continual Learning Semantic Segmentation

Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task

no code implementations1 Jun 2021 Longhui Wei, Lingxi Xie, Wengang Zhou, Houqiang Li, Qi Tian

By simply pulling the different augmented views of each image together or other novel mechanisms, they can learn much unsupervised knowledge and significantly improve the transfer performance of pre-training models.

Self-Supervised Learning

Conformer: Local Features Coupling Global Representations for Visual Recognition

3 code implementations ICCV 2021 Zhiliang Peng, Wei Huang, Shanzhi Gu, Lingxi Xie, YaoWei Wang, Jianbin Jiao, Qixiang Ye

Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations.

Image Classification Instance Segmentation +3

Visformer: The Vision-friendly Transformer

3 code implementations ICCV 2021 Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, Qi Tian

The past year has witnessed the rapid development of applying the Transformer module to vision problems.

Image Classification

Location-Sensitive Visual Recognition with Cross-IOU Loss

1 code implementation11 Apr 2021 Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang, Qi Tian

Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks.

Instance Segmentation Object Detection +2

MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes

no code implementations CVPR 2021 Zhikai Chen, Lingxi Xie, Shanmin Pang, Yong He, Bo Zhang

This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks.

Interactive Fusion of Multi-level Features for Compositional Activity Recognition

1 code implementation10 Dec 2020 Rui Yan, Lingxi Xie, Xiangbo Shu, Jinhui Tang

To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated.

Action Recognition

UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification

1 code implementation CVPR 2021 Tianyu Zhang, Lingxi Xie, Longhui Wei, Zijie Zhuang, Yongfei Zhang, Bo Li, Qi Tian

The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains.

Domain Adaptation Image Generation +1

Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning

no code implementations4 Dec 2020 Haohang Xu, Xiaopeng Zhang, Hao Li, Lingxi Xie, Hongkai Xiong, Qi Tian

In this paper, we propose a hierarchical semantic alignment strategy via expanding the views generated by a single image to \textbf{Cross-samples and Multi-level} representation, and models the invariance to semantically similar images in a hierarchical way.

Contrastive Learning Representation Learning +2

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.

Omni-GAN: On the Secrets of cGANs and Beyond

1 code implementation ICCV 2021 Peng Zhou, Lingxi Xie, Bingbing Ni, Cong Geng, Qi Tian

The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse.

Conditional Image Generation

Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations

no code implementations19 Nov 2020 Xinyue Huo, Lingxi Xie, Longhui Wei, Xiaopeng Zhang, Hao Li, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian

Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation.

Contrastive Learning Data Augmentation +1

Privileged Knowledge Distillation for Online Action Detection

no code implementations18 Nov 2020 Peisen Zhao, Lingxi Xie, Ya zhang, Yanfeng Wang, Qi Tian

Knowledge distillation is employed to transfer the privileged information from the offline teacher to the online student.

Action Detection Curriculum Learning +1

One-bit Supervision for Image Classification

1 code implementation NeurIPS 2020 Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian

Instead of training a model upon the accurate label of each sample, our setting requires the model to query with a predicted label of each sample and learn from the answer whether the guess is correct.

General Classification Image Classification

Corner Proposal Network for Anchor-free, Two-stage Object Detection

1 code implementation ECCV 2020 Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang, Qi Tian

On the MS-COCO dataset, CPN achieves an AP of 49. 2% which is competitive among state-of-the-art object detection methods.

Object Detection

Polar Relative Positional Encoding for Video-Language Segmentation

no code implementations20 Jul 2020 Ke Ning, Lingxi Xie, Fei Wu, Qi Tian

In this paper, we propose a novel Polar Relative Positional Encoding (PRPE) mechanism that represents spatial relations in a ``linguistic'' way, i. e., in terms of direction and range.

Referring Expression Segmentation

Social Adaptive Module for Weakly-supervised Group Activity Recognition

no code implementations ECCV 2020 Rui Yan, Lingxi Xie, Jinhui Tang, Xiangbo Shu, Qi Tian

This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.

Group Activity Recognition

Universal-to-Specific Framework for Complex Action Recognition

no code implementations13 Jul 2020 Peisen Zhao, Lingxi Xie, Ya zhang, Qi Tian

The U2S framework is composed of three subnetworks: a universal network, a category-specific network, and a mask network.

Action Recognition Decision Making

GOLD-NAS: Gradual, One-Level, Differentiable

1 code implementation7 Jul 2020 Kaifeng Bi, Lingxi Xie, Xin Chen, Longhui Wei, Qi Tian

There has been a large literature of neural architecture search, but most existing work made use of heuristic rules that largely constrained the search flexibility.

Image Classification Neural Architecture Search

Discretization-Aware Architecture Search

1 code implementation7 Jul 2020 Yunjie Tian, Chang Liu, Lingxi Xie, Jianbin Jiao, Qixiang Ye

The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods.

Image Classification Neural Architecture Search

Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks

1 code implementation25 Jun 2020 Peng Zhou, Lingxi Xie, Xiaopeng Zhang, Bingbing Ni, Qi Tian

To learn the sampling policy, a Markov decision process is embedded into the search algorithm and a moving average is applied for better stability.

Image Generation

ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation

no code implementations24 Jun 2020 Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Qi Tian

This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself.

Autonomous Driving Medical Image Segmentation

Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks

no code implementations17 Apr 2020 Xin Chen, Lingxi Xie, Jun Wu, Longhui Wei, Yuhui Xu, Qi Tian

We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal.

Graph Convolutional Network Neural Architecture Search

Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing

1 code implementation CVPR 2020 Hengtong Hu, Lingxi Xie, Richang Hong, Qi Tian

In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval.

Knowledge Distillation

Circumventing Outliers of AutoAugment with Knowledge Distillation

no code implementations ECCV 2020 Longhui Wei, An Xiao, Lingxi Xie, Xin Chen, Xiaopeng Zhang, Qi Tian

AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization.

Data Augmentation General Classification +1

Bottom-Up Temporal Action Localization with Mutual Regularization

1 code implementation ECCV 2020 Peisen Zhao, Lingxi Xie, Chen Ju, Ya zhang, Yan-Feng Wang, Qi Tian

To alleviate this problem, we introduce two regularization terms to mutually regularize the learning procedure: the Intra-phase Consistency (IntraC) regularization is proposed to make the predictions verified inside each phase; and the Inter-phase Consistency (InterC) regularization is proposed to keep consistency between these phases.

Temporal Action Localization

Latency-Aware Differentiable Neural Architecture Search

1 code implementation17 Jan 2020 Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Bowen Shi, Qi Tian, Hongkai Xiong

However, these methods suffer the difficulty in optimizing network, so that the searched network is often unfriendly to hardware.

Neural Architecture Search

Wasserstein-Bounded Generative Adversarial Networks

no code implementations ICLR 2020 Peng Zhou, Bingbing Ni, Lingxi Xie, Xiaopeng Zhang, Hang Wang, Cong Geng, Qi Tian

In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.

Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild

1 code implementation23 Dec 2019 Xin Chen, Lingxi Xie, Jun Wu, Qi Tian

With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks.

Neural Architecture Search

Appending Adversarial Frames for Universal Video Attack

no code implementations10 Dec 2019 Zhikai Chen, Lingxi Xie, Shanmin Pang, Yong He, Qi Tian

There have been many efforts in attacking image classification models with adversarial perturbations, but the same topic on video classification has not yet been thoroughly studied.

General Classification Image Classification +1

Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters

1 code implementation25 Oct 2019 Kaifeng Bi, Changping Hu, Lingxi Xie, Xin Chen, Longhui Wei, Qi Tian

Our approach bridges the gap from two aspects, namely, amending the estimation on the architectural gradients, and unifying the hyper-parameter settings in the search and re-training stages.

Neural Architecture Search

Pruning from Scratch

1 code implementation27 Sep 2019 Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, Xiaolin Hu

Network pruning is an important research field aiming at reducing computational costs of neural networks.

Network Pruning

Single Camera Training for Person Re-identification

1 code implementation24 Sep 2019 Tianyu Zhang, Lingxi Xie, Longhui Wei, Yongfei Zhang, Bo Li, Qi Tian

Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera.

Metric Learning Person Re-Identification

Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data

no code implementations19 Sep 2019 Zhuoxun He, Lingxi Xie, Xin Chen, Ya zhang, Yan-Feng Wang, Qi Tian

Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks.

Data Augmentation Image Classification +1

PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

6 code implementations ICLR 2020 Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, Hongkai Xiong

Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture.

Neural Architecture Search

Defending Adversarial Attacks by Correcting logits

no code implementations26 Jun 2019 Yifeng Li, Lingxi Xie, Ya zhang, Rui Zhang, Yanfeng Wang, Qi Tian

Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning.

Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation

3 code implementations ICCV 2019 Xin Chen, Lingxi Xie, Jun Wu, Qi Tian

Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search.

Neural Architecture Search

CenterNet: Keypoint Triplets for Object Detection

10 code implementations ICCV 2019 Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, Qi Tian

In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions.

Object Detection

Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval

1 code implementation ICCV 2019 Qing Liu, Lingxi Xie, Huiyu Wang, Alan Yuille

Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications.

Domain Adaptation Sketch-Based Image Retrieval +1

Thickened 2D Networks for Efficient 3D Medical Image Segmentation

no code implementations2 Apr 2019 Qihang Yu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method's effectiveness and ability in capturing 3D information.

Medical Image Segmentation

SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images

2 code implementations2 Jan 2019 Caijing Miao, Lingxi Xie, Fang Wan, Chi Su, Hongye Liu, Jianbin Jiao, Qixiang Ye

In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.

Object Localization

Identity-Enhanced Network for Facial Expression Recognition

no code implementations11 Dec 2018 Yanwei Li, Xingang Wang, Shilei Zhang, Lingxi Xie, Wenqi Wu, Hongyuan Yu, Zheng Zhu

Facial expression recognition is a challenging task, arguably because of large intra-class variations and high inter-class similarities.

Facial Expression Recognition Multi-Task Learning

Attention-guided Unified Network for Panoptic Segmentation

no code implementations CVPR 2019 Yanwei Li, Xinze Chen, Zheng Zhu, Lingxi Xie, Guan Huang, Dalong Du, Xingang Wang

This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level.

Panoptic Segmentation

Elastic Boundary Projection for 3D Medical Image Segmentation

2 code implementations CVPR 2019 Tianwei Ni, Lingxi Xie, Huangjie Zheng, Elliot K. Fishman, Alan L. Yuille

The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface.

3D Medical Imaging Segmentation

CRAVES: Controlling Robotic Arm with a Vision-based Economic System

1 code implementation CVPR 2019 Yiming Zuo, Weichao Qiu, Lingxi Xie, Fangwei Zhong, Yizhou Wang, Alan L. Yuille

We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world.

3D Pose Estimation Domain Adaptation

Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

1 code implementation CVPR 2019 Chen Wei, Lingxi Xie, Xutong Ren, Yingda Xia, Chi Su, Jiaying Liu, Qi Tian, Alan L. Yuille

We consider spatial contexts, for which we solve so-called jigsaw puzzles, i. e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration.

General Classification Image Classification +3

Snapshot Distillation: Teacher-Student Optimization in One Generation

no code implementations CVPR 2019 Chenglin Yang, Lingxi Xie, Chi Su, Alan L. Yuille

Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting.

Image Classification Object Detection +1

Generalized Coarse-to-Fine Visual Recognition with Progressive Training

no code implementations29 Nov 2018 Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, Qi Tian, Elliot K. Fishman, Alan L. Yuille

Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn.

Curriculum Learning Image Classification +2

Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data

1 code implementation ICCV 2019 Yutong Bai, Qing Liu, Lingxi Xie, Weichao Qiu, Yan Zheng, Alan Yuille

In particular, this enables images in the training dataset to be matched to a virtual 3D model of the object (for simplicity, we assume that the object viewpoint can be estimated by standard techniques).

Semantic Part Detection

Phase Collaborative Network for Two-Phase Medical Image Segmentation

no code implementations28 Nov 2018 Huangjie Zheng, Lingxi Xie, Tianwei Ni, Ya zhang, Yan-Feng Wang, Qi Tian, Elliot K. Fishman, Alan L. Yuille

However, in medical image analysis, fusing prediction from two phases is often difficult, because (i) there is a domain gap between two phases, and (ii) the semantic labels are not pixel-wise corresponded even for images scanned from the same patient.

Medical Image Segmentation

Accelerating Deep Neural Networks with Spatial Bottleneck Modules

no code implementations7 Sep 2018 Junran Peng, Lingxi Xie, Zhao-Xiang Zhang, Tieniu Tan, Jingdong Wang

This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks.

Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images

no code implementations7 Sep 2018 Xiaolu Zhang, Shiwan Zhao, Lingxi Xie

This paper considers WCE-based gastric ulcer detection, in which the major challenge is to detect the lesions in a local region.

Curriculum Learning

Attention-based Pyramid Aggregation Network for Visual Place Recognition

no code implementations1 Aug 2018 Yingying Zhu, Jiong Wang, Lingxi Xie, Liang Zheng

Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task.

Image Retrieval Visual Place Recognition

Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

no code implementations9 Jul 2018 Zhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans.

General Classification

G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

no code implementations30 Jun 2018 Bingzhe Wu, Xiaolu Zhang, Shiwan Zhao, Lingxi Xie, Caihong Zeng, Zhihong Liu, Guangyu Sun

Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).

Decision Making General Classification +1

Joint Shape Representation and Classification for Detecting PDAC

no code implementations27 Apr 2018 Fengze Liu, Lingxi Xie, Yingda Xia, Elliot K. Fishman, Alan L. Yuille

Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting.

General Classification

Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

no code implementations ECCV 2018 Yan Wang, Lingxi Xie, Siyuan Qiao, Ya zhang, Wenjun Zhang, Alan L. Yuille

Convolution is spatially-symmetric, i. e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recognition.

General Classification Image Classification

Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net

no code implementations2 Apr 2018 Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman, Alan L. Yuille

There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation.

SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data

no code implementations1 Apr 2018 Qi Chen, Weichao Qiu, Yi Zhang, Lingxi Xie, Alan Yuille

But, this raises an important problem in active vision: given an {\bf infinite} data space, how to effectively sample a {\bf finite} subset to train a visual classifier?

General Classification

Adversarial Attacks Beyond the Image Space

no code implementations CVPR 2019 Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi Keung Tang, Alan L. Yuille

Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.

Question Answering Visual Question Answering

Visual Concepts and Compositional Voting

no code implementations13 Nov 2017 Jianyu Wang, Zhishuai Zhang, Cihang Xie, Yuyin Zhou, Vittal Premachandran, Jun Zhu, Lingxi Xie, Alan Yuille

We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles.

Semantic Part Detection

DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion

no code implementations CVPR 2018 Zhishuai Zhang, Cihang Xie, Jian-Yu Wang, Lingxi Xie, Alan L. Yuille

The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts.

Semantic Part Detection

Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

2 code implementations CVPR 2018 Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille

The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.

Pancreas Segmentation

Detecting Semantic Parts on Partially Occluded Objects

no code implementations25 Jul 2017 Jianyu Wang, Cihang Xie, Zhishuai Zhang, Jun Zhu, Lingxi Xie, Alan Yuille

Our approach detects semantic parts by accumulating the confidence of local visual cues.

Semantic Part Detection

Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

no code implementations22 Jun 2017 Yuyin Zhou, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille

Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation.

Pancreas Segmentation

Adversarial Examples for Semantic Segmentation and Object Detection

2 code implementations ICCV 2017 Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, Alan Yuille

Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e. g., the basic target is a pixel or a receptive field in segmentation, and an object proposal in detection), which inspires us to optimize a loss function over a set of pixels/proposals for generating adversarial perturbations.

Adversarial Attack Object Detection +1

SORT: Second-Order Response Transform for Visual Recognition

no code implementations ICCV 2017 Yan Wang, Lingxi Xie, Chenxi Liu, Ya zhang, Wenjun Zhang, Alan Yuille

In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks.

Genetic CNN

1 code implementation ICCV 2017 Lingxi Xie, Alan Yuille

The deep Convolutional Neural Network (CNN) is the state-of-the-art solution for large-scale visual recognition.

Object Recognition

Deep Collaborative Learning for Visual Recognition

no code implementations3 Mar 2017 Yan Wang, Lingxi Xie, Ya zhang, Wenjun Zhang, Alan Yuille

We formulate the function of a convolutional layer as learning a large visual vocabulary, and propose an alternative way, namely Deep Collaborative Learning (DCL), to reduce the computational complexity.

General Classification Image Classification

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

Object Recognition with and without Objects

1 code implementation20 Nov 2016 Zhuotun Zhu, Lingxi Xie, Alan L. Yuille

While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image.

Object Recognition

Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons

no code implementations21 Jul 2016 Lingxi Xie, Qi Tian, John Flynn, Jingdong Wang, Alan Yuille

For this, we consider the neurons in the hidden layer as neural words, and construct a set of geometric neural phrases on top of them.

Image Classification

InterActive: Inter-Layer Activeness Propagation

no code implementations CVPR 2016 Lingxi Xie, Liang Zheng, Jingdong Wang, Alan Yuille, Qi Tian

An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network.

General Classification

DisturbLabel: Regularizing CNN on the Loss Layer

2 code implementations CVPR 2016 Lingxi Xie, Jingdong Wang, Zhen Wei, Meng Wang, Qi Tian

During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc.

Data Augmentation

RIDE: Reversal Invariant Descriptor Enhancement

no code implementations ICCV 2015 Lingxi Xie, Jingdong Wang, Weiyao Lin, Bo Zhang, Qi Tian

In many fine-grained object recognition datasets, image orientation (left/right) might vary from sample to sample.

Object Recognition

Fidelity-Naturalness Evaluation of Single Image Super Resolution

no code implementations21 Nov 2015 Xuan Dong, Yu Zhu, Weixin Li, Lingxi Xie, Alex Wong, Alan Yuille

In this paper, we proposed to use both fidelity (the difference with original images) and naturalness (human visual perception of super resolved images) for evaluation.

Image Quality Assessment Image Super-Resolution

Orientational Pyramid Matching for Recognizing Indoor Scenes

no code implementations CVPR 2014 Lingxi Xie, Jingdong Wang, Baining Guo, Bo Zhang, Qi Tian

The novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid.

General Classification Scene Classification +1

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