Search Results for author: Le Zhang

Found 31 papers, 15 papers with code

Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

1 code implementation6 Oct 2021 Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang

Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).

Curriculum Learning Traveling Salesman Problem

Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net

no code implementations17 Aug 2021 Yu Qiu, Yun Liu, Le Zhang, Jing Xu

The asymmetric bilateral encoder has a transformer path and a lightweight CNN path, where the two paths communicate at each encoder stage to learn complementary global contexts and local spatial details, respectively.

Object Detection Salient Object Detection

Free Lunch for Co-Saliency Detection: Context Adjustment

no code implementations4 Aug 2021 Lingdong Kong, Prakhar Ganesh, Tan Wang, Junhao Liu, Le Zhang, Yao Chen

We hope that the scale, diversity, and quality of our dataset can benefit researchers in this area and beyond.

Saliency Detection Semantic Segmentation

Transformer in Convolutional Neural Networks

1 code implementation6 Jun 2021 Yun Liu, Guolei Sun, Yu Qiu, Le Zhang, Ajad Chhatkuli, Luc van Gool

We tackle the low-efficiency flaw of vision transformer caused by the high computational/space complexity in Multi-Head Self-Attention (MHSA).

Unsupervised Scale-consistent Depth Learning from Video

1 code implementation25 May 2021 Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid

We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.

Monocular Depth Estimation Monocular Visual Odometry +1

EDN: Salient Object Detection via Extremely-Downsampled Network

1 code implementation24 Dec 2020 Yu-Huan Wu, Yun Liu, Le Zhang, Ming-Ming Cheng, Bo Ren

To accomplish better multi-level feature fusion, we construct the Scale-Correlated Pyramid Convolution (SCPC) to build an elegant decoder for recovering object details from the above extreme downsampling.

Object Detection Object Localization +1

Disentangling Human Error from Ground Truth in Segmentation of Medical Images

1 code implementation NeurIPS 2020 Le Zhang, Ryutaro Tanno, MouCheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel Alexander

In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large.

Medical Image Segmentation

Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow

1 code implementation1 Sep 2020 Yu-Chao Gu, Le Zhang, Yun Liu, Shao-Ping Lu, Ming-Ming Cheng

Recent generative methods formulate GZSL as a missing data problem, which mainly adopts GANs or VAEs to generate visual features for unseen classes.

Generalized Zero-Shot Learning

Regularized Densely-connected Pyramid Network for Salient Instance Segmentation

1 code implementation28 Aug 2020 Yu-Huan Wu, Yun Liu, Le Zhang, Wang Gao, Ming-Ming Cheng

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels.

Instance Segmentation RGB Salient Object Detection +2

The 2019 BBN Cross-lingual Information Retrieval System

no code implementations LREC 2020 Le Zhang, Damianos Karakos, William Hartmann, Manaj Srivastava, Lee Tarlin, David Akodes, Sanjay Krishna Gouda, Numra Bathool, Lingjun Zhao, Zhuolin Jiang, Richard Schwartz, John Makhoul

In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.

Information Retrieval Machine Translation +2

Ordered or Orderless: A Revisit for Video based Person Re-Identification

no code implementations24 Dec 2019 Le Zhang, Zenglin Shi, Joey Tianyi Zhou, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Zeng Zeng, Chunhua Shen

Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.

Video-Based Person Re-Identification

AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning

no code implementations27 Nov 2019 Xin-Yu Zhang, Le Zhang, Zao-Yi Zheng, Yun Liu, Jia-Wang Bian, Ming-Ming Cheng

The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.

A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detection

2 code implementations18 Oct 2019 Houwang Zhang, Chong Wu, Le Zhang, Hanying Zheng

Then according to the statistical features of noise, we propose a novel centroid update approach to enhance the robustness of clustering-based superpixel methods.

Edge Detection

An Evaluation of Feature Matchers for Fundamental Matrix Estimation

no code implementations26 Aug 2019 Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid

According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.

Robust Regression via Deep Negative Correlation Learning

no code implementations24 Aug 2019 Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, Zeng Zeng

Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).

Age Estimation Crowd Counting +1

A Deep Framework for Bone Age Assessment based on Finger Joint Localization

no code implementations7 May 2019 Xiaoman Zhang, Ziyuan Zhao, Cen Chen, Songyou Peng, Min Wu, Zhongyao Cheng, Singee Teo, Le Zhang, Zeng Zeng

In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.

High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

no code implementations10 Jan 2019 Rahman Attar, Marco Pereanez, Ali Gooya, Xenia Alba, Le Zhang, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset.

Image Quality Assessment

Salient Object Detection via High-to-Low Hierarchical Context Aggregation

no code implementations28 Dec 2018 Yun Liu, Yu Qiu, Le Zhang, Jia-Wang Bian, Guang-Yu Nie, Ming-Ming Cheng

In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features.

RGB Salient Object Detection Saliency Prediction +1

Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

no code implementations6 Nov 2018 Le Zhang, Ali Gooya, Marco Pereanez, Bo Dong, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment.

MatchBench: An Evaluation of Feature Matchers

no code implementations7 Aug 2018 Jia-Wang Bian, Ruihan Yang, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid, WenHai Wu

This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly.

Structure from Motion

Learning Pixel-wise Labeling from the Internet without Human Interaction

no code implementations19 May 2018 Yun Liu, Yujun Shi, Jia-Wang Bian, Le Zhang, Ming-Ming Cheng, Jiashi Feng

Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.

Semantic Segmentation

A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues

1 code implementation2 May 2018 Songyou Peng, Le Zhang, Yutong Ban, Meng Fang, Stefan Winkler

In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.

Semantic Edge Detection with Diverse Deep Supervision

2 code implementations9 Apr 2018 Yun Liu, Ming-Ming Cheng, Deng-Ping Fan, Le Zhang, JiaWang Bian, DaCheng Tao

Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.

Edge Detection Object Proposal Generation +2

Image Matching: An Application-oriented Benchmark

no code implementations12 Sep 2017 Jia-Wang Bian, Le Zhang, Yun Liu, Wen-Yan Lin, Ming-Ming Cheng, Ian D. Reid

To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods.

Kernel Cross-Correlator

2 code implementations12 Sep 2017 Chen Wang, Le Zhang, Lihua Xie, Junsong Yuan

Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking.

Activity Recognition Object Detection +1

Robust Visual Tracking Using Oblique Random Forests

1 code implementation CVPR 2017 Le Zhang, Jagannadan Varadarajan, Ponnuthurai Nagaratnam Suganthan, Narendra Ahuja, Pierre Moulin

Unlike conventional orthogonal decision trees that use a single feature and heuristic measures to obtain a split at each node, we propose to use a more powerful proximal SVM to obtain oblique hyperplanes to capture the geometric structure of the data better.

General Classification Image Classification +4

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