1k citations · 2.5k across the 64 of their papers we have counts for
65 papers
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation
Zhusi Zhong, Jie Li, Lulu Bi +6
Medical image segmentation based on deep learning often fails when deployed on images from a different domain. The domain adaptation methods aim to solve domain-shift challenges, b…
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
Haojun Xu, Yan Gao, Zheng Hui +2
How humans understand and recognize the actions of others is a complex neuroscientific problem that involves a combination of cognitive mechanisms and neural networks. Research has…
Multi-View Clustering via Semi-non-negative Tensor Factorization
Jing Li, Quanxue Gao, Qianqian Wang +2
Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in cl…
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
Qihuang Zhong, Liang Ding, Yibing Zhan +11
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language…
Seeking Subjectivity in Visual Emotion Distribution Learning
Jingyuan Yang, Jie Li, Leida Li +3
Visual Emotion Analysis (VEA), which aims to predict people's emotions towards different visual stimuli, has become an attractive research topic recently. Rather than a single labe…
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
De Cheng, Tongliang Liu, Yixiong Ning +5
In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifie…
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation
Yue Zhao, Lingming Zhang, Yang Liu +6
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods…
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
De Cheng, Yan Li, Dingwen Zhang +3
Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to…
Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
Hangyu Li, Nannan Wang, Xi Yang +2
Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e.…
Semi-parametric Makeup Transfer via Semantic-aware Correspondence
Mingrui Zhu, Yun Yi, Nannan Wang +2
The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer. Conventional approaches for makeup transfe…
SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images
Lin Qi, Feng Gao, Junyu Dong +2
Linear spectral unmixing is an essential technique in hyperspectral image processing and interpretation. In recent years, deep learning-based approaches have shown great promise in…
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
Shuyin Xia, Xiaochuan Dai, Guoyin Wang +2
Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. T…
Image-specific Convolutional Kernel Modulation for Single Image Super-resolution
Yuanfei Huang, Jie Li, Yanting Hu +2
Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samp…
SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network
Jingyuan Yang, Xinbo Gao, Leida Li +2
Visual Emotion Analysis (VEA) aims at finding out how people feel emotionally towards different visual stimuli, which has attracted great attention recently with the prevalence of…
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id
De Cheng, Jingyu Zhou, Nannan Wang +1
Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its practical application in the read-world video surveillance system. The traditional unsup…
Single Image Dehazing with An Independent Detail-Recovery Network
Yan Li, De Cheng, Jiande Sun +3
Single image dehazing is a prerequisite which affects the performance of many computer vision tasks and has attracted increasing attention in recent years. However, most existing d…
Stimuli-Aware Visual Emotion Analysis
Jingyuan Yang, Jie Li, Xiumei Wang +2
Visual emotion analysis (VEA) has attracted great attention recently, due to the increasing tendency of expressing and understanding emotions through images on social networks. Dif…
Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds
Fangcen Liu, Chenqiang Gao, Fang Chen +3
The infrared small-dim target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared small-dim targets sprea…
Support-Set Based Cross-Supervision for Video Grounding
Xinpeng Ding, Nannan Wang, Shiwei Zhang +5
Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to…
Effective and Efficient Graph Learning for Multi-view Clustering
Quanxue Gao, Wei Xia, Xinbo Gao +3
Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering…
A Circular-Structured Representation for Visual Emotion Distribution Learning
Jingyuan Yang, Jie Li, Leida Li +2
Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks. Since human emotions are ambiguous and subjectiv…
Towards Defending against Adversarial Examples via Attack-Invariant Features
Dawei Zhou, Tongliang Liu, Bo Han +3
Deep neural networks (DNNs) are vulnerable to adversarial noise. Their adversarial robustness can be improved by exploiting adversarial examples. However, given the continuously ev…
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are vulnerable to adversarial noise. A range of adversarial defense techniques have been proposed to mitigate the interference of adversarial noise, amo…
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Andres Romero, Heewon Kim +28
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions hav…