105 citations · 225 across the 31 of their papers we have counts for
31 papers
Private-Library-Oriented Code Generation with Large Language Models
Daoguang Zan, Bei Chen, Yongshun Gong +6
Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. Th…
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation
Ren Wang, Haoliang Sun, Qi Wei +3
Meta-learning methods typically follow a two-loop framework, where each loop potentially suffers from notorious overfitting, hindering rapid adaptation and generalization to new ta…
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
Qiangchang Wang, Yilong Yin
Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the de…
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
Guoqiang Wu, Chongxuan Li, Yilong Yin
Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label learning and is commonly used in practice. However, its theoretical understanding is far lacking. Toward solv…
MetaViewer: Towards A Unified Multi-View Representation
Ren Wang, Haoliang Sun, Yuling Ma +2
Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to o…
Fine-Grained Classification with Noisy Labels
Qi Wei, Lei Feng, Haoliang Sun +3
Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine-gra…
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection
Rundong He, Rongxue Li, Zhongyi Han +1
Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually m…
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
Qi Wei, Haoliang Sun, Xiankai Lu +1
Sample selection is an effective strategy to mitigate the effect of label noise in robust learning. Typical strategies commonly apply the small-loss criterion to identify clean sam…
DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement
Yuhang Dong, Gongping Yang, Yilong Yin
Remote photoplethysmography (rPPG) based physiological measurement has great application values in affective computing, non-contact health monitoring, telehealth monitoring, etc, w…
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning
Ziqi Zhou, Li Lian, Yilong Yin +1
Network compression is crucial to making the deep networks to be more efficient, faster, and generalizable to low-end hardware. Current network compression methods have two open pr…
Active Source Free Domain Adaptation
Fan Wang, Zhongyi Han, Zhiyan Zhang +1
Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an ef…
Exploring Linear Feature Disentanglement For Neural Networks
Tiantian He, Zhibin Li, Yongshun Gong +3
Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs). Due to the complex non-linear characteristic of samples, the o…
Series Photo Selection via Multi-view Graph Learning
Jin Huang, Lu Zhang, Yongshun Gong +3
Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos. Whil…
Sequential Multi-task Learning with Task Dependency for Appeal Judgment Prediction
Lianxin Song, Xiaohui Han, Guangqi Liu +3
Legal Judgment Prediction (LJP) aims to automatically predict judgment results, such as charges, relevant law articles, and the term of penalty. It plays a vital role in legal assi…
Learning to Rectify for Robust Learning with Noisy Labels
Haoliang Sun, Chenhui Guo, Qi Wei +2
Label noise significantly degrades the generalization ability of deep models in applications. Effective strategies and approaches, \textit{e.g.} re-weighting, or loss correction, a…
Learning Transferable Parameters for Unsupervised Domain Adaptation
Zhongyi Han, Haoliang Sun, Yilong Yin
Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a labeled source domain to an unlabeled domain under the distribution shift. Thanks to the strong repr…
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Qian Yu, Lei Qi, Luping Zhou +5
Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these…
Tri-Branch Convolutional Neural Networks for Top- Focused Academic Performance Prediction
Chaoran Cui, Jian Zong, Yuling Ma +4
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, su…
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
Chaoran Cui, Xiaojie Li, Juan Du +4
Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. In this paper, we present…
MetaKernel: Learning Variational Random Features with Limited Labels
Yingjun Du, Haoliang Sun, Xiantong Zhen +4
Few-shot learning deals with the fundamental and challenging problem of learning from a few annotated samples, while being able to generalize well on new tasks. The crux of few-sho…
Attentional Prototype Inference for Few-Shot Segmentation
Haoliang Sun, Xiankai Lu, Haochen Wang +4
This paper aims to address few-shot segmentation. While existing prototype-based methods have achieved considerable success, they suffer from uncertainty and ambiguity caused by li…
A Survey on Natural Language Video Localization
Xinfang Liu, Xiushan Nie, Zhifang Tan +2
Natural language video localization (NLVL), which aims to locate a target moment from a video that semantically corresponds to a text query, is a novel and challenging task. Toward…
Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression
Haoliang Sun, Xiantong Zhen, Chris Bailey +3
The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment. Conventional measurement of these angles suffers…
Attention Model Enhanced Network for Classification of Breast Cancer Image
Xiao Kang, Xingbo Liu, Xiushan Nie +2
Breast cancer classification remains a challenging task due to inter-class ambiguity and intra-class variability. Existing deep learning-based methods try to confront this challeng…