387 citations · 2k across the 64 of their papers we have counts for
65 papers
Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment
Sen Cui, Weishen Pan, Changshui Zhang +1
Algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this paper, we focus on the bipartite ranking scenario, where the in…
Rethinking Audio-visual Synchronization for Active Speaker Detection
Abudukelimu Wuerkaixi, You Zhang, Zhiyao Duan +1
Active speaker detection (ASD) systems are important modules for analyzing multi-talker conversations. They aim to detect which speakers or none are talking in a visual scene at an…
Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking
Peng Dai, Yiqiang Feng, Renliang Weng +1
The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. In this paper, we propose a novel solution named T…
VD-PCR: Improving Visual Dialog with Pronoun Coreference Resolution
Xintong Yu, Hongming Zhang, Ruixin Hong +2
The visual dialog task requires an AI agent to interact with humans in multi-round dialogs based on a visual environment. As a common linguistic phenomenon, pronouns are often used…
Searching for Network Width with Bilaterally Coupled Network
Xiu Su, Shan You, Jiyang Xie +4
Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…
Weak Augmentation Guided Relational Self-Supervised Learning
Mingkai Zheng, Shan You, Fei Wang +4
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most m…
A Theoretical View of Linear Backpropagation and Its Convergence
Ziang Li, Yiwen Guo, Haodi Liu +1
Backpropagation (BP) is widely used for calculating gradients in deep neural networks (DNNs). Applied often along with stochastic gradient descent (SGD) or its variants, BP is cons…
Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding
Ziang Li, Kailun Wu, Yiwen Guo +1
Drawing on theoretical insights, we advocate an error-based thresholding (EBT) mechanism for learned ISTA (LISTA), which utilizes a function of the layer-wise reconstruction error…
GreedyNASv2: Greedier Search with a Greedy Path Filter
Tao Huang, Shan You, Fei Wang +4
Training a good supernet in one-shot NAS methods is difficult since the search space is usually considerably huge (e.g., ). In order to enhance the supernet's evaluation a…
Weakly Supervised Contrastive Learning
Mingkai Zheng, Fei Wang, Shan You +4
Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the exis…
CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization
Wenzheng Hu, Zhengping Che, Ning Liu +4
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have…
Correcting the User Feedback-Loop Bias for Recommendation Systems
Weishen Pan, Sen Cui, Hongyi Wen +3
Selection bias is prevalent in the data for training and evaluating recommendation systems with explicit feedback. For example, users tend to rate items they like. However, when ra…
Exophoric Pronoun Resolution in Dialogues with Topic Regularization
Xintong Yu, Hongming Zhang, Yangqiu Song +3
Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem. Previous works on pronoun coreference resolution (PCR) mostly f…
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition
Weishen Pan, Sen Cui, Jiang Bian +2
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the devel…
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
Song Tang, Yan Yang, Zhiyuan Ma +5
In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase. However, in many real-world scenarios, owing to some re…
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
Sen Cui, Weishen Pan, Jian Liang +2
Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples acro…
Collaboration Equilibrium in Federated Learning
Sen Cui, Jian Liang, Weishen Pan +3
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy. Recently, there have…
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
Mingkai Zheng, Shan You, Fei Wang +4
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most o…
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
Xiu Su, Shan You, Mingkai Zheng +4
In one-shot weight sharing for NAS, the weights of each operation (at each layer) are supposed to be identical for all architectures (paths) in the supernet. However, this rules ou…
ViTAS: Vision Transformer Architecture Search
Xiu Su, Shan You, Jiyang Xie +6
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks. One of the simplest solutions i…
The Definitions of Interpretability and Learning of Interpretable Models
Weishen Pan, Changshui Zhang
As machine learning algorithms getting adopted in an ever-increasing number of applications, interpretation has emerged as a crucial desideratum. In this paper, we propose a mathem…
BCNet: Searching for Network Width with Bilaterally Coupled Network
Xiu Su, Shan You, Fei Wang +3
Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…
Learning a Proposal Classifier for Multiple Object Tracking
Peng Dai, Renliang Weng, Wongun Choi +3
The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-assoc…
Prioritized Architecture Sampling with Monto-Carlo Tree Search
Xiu Su, Tao Huang, Yanxi Li +5
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. Ho…