420 citations · 2.4k across the 126 of their papers we have counts for
19 papers · 1 filter
Identity-Enhanced Network for Facial Expression Recognition
Yanwei Li, Xingang Wang, Shilei Zhang +4
Facial expression recognition is a challenging task, arguably because of large intra-class variations and high inter-class similarities. The core drawback of the existing approache…
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning
Chen Wei, Lingxi Xie, Xutong Ren +5
Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We conside…
Attention-guided Unified Network for Panoptic Segmentation
Yanwei Li, Xinze Chen, Zheng Zhu +4
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…
CRAVES: Controlling Robotic Arm with a Vision-based Economic System
Yiming Zuo, Weichao Qiu, Lingxi Xie +3
Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algori…
Elastic Boundary Projection for 3D Medical Image Segmentation
Tianwei Ni, Lingxi Xie, Huangjie Zheng +2
We focus on an important yet challenging problem: using a 2D deep network to deal with 3D segmentation for medical image analysis. Existing approaches either applied multi-view pla…
Snapshot Distillation: Teacher-Student Optimization in One Generation
Chenglin Yang, Lingxi Xie, Chi Su +1
Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. Teacher-student optimization aims at providin…
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Xutong Ren, Lingxi Xie, Chen Wei +6
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn. Coarse-to-fine (C2F)…
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
Yutong Bai, Qing Liu, Lingxi Xie +3
Detecting semantic parts of an object is a challenging task in computer vision, particularly because it is hard to construct large annotated datasets due to the difficulty of annot…
Phase Collaborative Network for Two-Phase Medical Image Segmentation
Huangjie Zheng, Lingxi Xie, Tianwei Ni +5
In real-world practice, medical images acquired in different phases possess complementary information, {\em e.g.}, radiologists often refer to both arterial and venous scans in ord…
Accelerating Deep Neural Networks with Spatial Bottleneck Modules
Junran Peng, Lingxi Xie, Zhaoxiang Zhang +2
This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks. The core idea is to decompose convolution into t…
Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images
Xiaolu Zhang, Shiwan Zhao, Lingxi Xie
The Wireless Capsule Endoscopy (WCE) is becoming a popular way of screening gastrointestinal system diseases and cancer. However, the time-consuming process in inspecting WCE data…
Attention-based Pyramid Aggregation Network for Visual Place Recognition
Yingying Zhu, Jiong Wang, Lingxi Xie +1
Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist th…
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Bingzhe Wu, Xiaolu Zhang, Shiwan Zhao +4
Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides…
Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma
Zhuotun Zhu, Yingda Xia, Lingxi Xie +2
We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named…
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
Chenglin Yang, Lingxi Xie, Siyuan Qiao +1
We focus on the problem of training a deep neural network in generations. The flowchart is that, in order to optimize the target network (student), another network (teacher) with t…
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Yan Wang, Lingxi Xie, Siyuan Qiao +3
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 recogniti…
Joint Shape Representation and Classification for Detecting PDAC
Fengze Liu, Lingxi Xie, Yingda Xia +2
We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D volume classification task w…
Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
Yingda Xia, Lingxi Xie, Fengze Liu +3
There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this pa…
SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data
Qi Chen, Weichao Qiu, Yi Zhang +2
State-of-the-art techniques of artificial intelligence, in particular deep learning, are mostly data-driven. However, collecting and manually labeling a large scale dataset is both…