92 citations · 318 across the 29 of their papers we have counts for
30 papers
Graph Convolution Based Efficient Re-Ranking for Visual Retrieval
Yuqi Zhang, Qi Qian, Hongsong Wang +3
Visual retrieval tasks such as image retrieval and person re-identification (Re-ID) aim at effectively and thoroughly searching images with similar content or the same identity. Af…
Improved Visual Fine-tuning with Natural Language Supervision
Junyang Wang, Yuanhong Xu, Juhua Hu +3
Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks wit…
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training
Qinghao Ye, Guohai Xu, Ming Yan +4
Video-language pre-training has advanced the performance of various downstream video-language tasks. However, most previous methods directly inherit or adapt typical image-language…
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization
Mengzhu Wang, Jianlong Yuan, Qi Qian +2
Domain generalization (DG) aims to learn a model on one or more different but related source domains that could be generalized into an unseen target domain. Existing DG methods try…
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation
Ziquan Liu, Yi Xu, Yuanhong Xu +5
The performance of machine learning models under distribution shift has been the focus of the community in recent years. Most of current methods have been proposed to improve the r…
Improved Fine-Tuning by Better Leveraging Pre-Training Data
Ziquan Liu, Yi Xu, Yuanhong Xu +5
As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studie…
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye +5
While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use eith…
Graph Convolution for Re-ranking in Person Re-identification
Yuqi Zhang, Qian Qi, Chong Liu +4
Nowadays, deep learning is widely applied to extract features for similarity computation in person re-identification (re-ID) and have achieved great success. However, due to the no…
Why Does Multi-Epoch Training Help?
Yi Xu, Qi Qian, Hao Li +1
Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in e…
Unsupervised Visual Representation Learning by Online Constrained K-Means
Qi Qian, Yuanhong Xu, Juhua Hu +2
Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to as…
A Theoretical Analysis of Learning with Noisily Labeled Data
Yi Xu, Qi Qian, Hao Li +1
Noisy labels are very common in deep supervised learning. Although many studies tend to improve the robustness of deep training for noisy labels, rare works focus on theoretically…
Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
Qiang Zhou, Chaohui Yu, Zhibin Wang +2
Supervised learning based object detection frameworks demand plenty of laborious manual annotations, which may not be practical in real applications. Semi-supervised object detecti…
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
Ming Lin, Pichao Wang, Zhenhong Sun +5
Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation.…
WeMix: How to Better Utilize Data Augmentation
Yi Xu, Asaf Noy, Ming Lin +3
Data augmentation is a widely used training trick in deep learning to improve the network generalization ability. Despite many encouraging results, several recent studies did point…
Semi-Anchored Detector for One-Stage Object Detection
Lei Chen, Qi Qian, Hao Li
A standard one-stage detector is comprised of two tasks: classification and regression. Anchors of different shapes are introduced for each location in the feature map to mitigate…
Improved Knowledge Distillation via Full Kernel Matrix Transfer
Qi Qian, Hao Li, Juhua Hu
Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model…
Neural Architecture Design for GPU-Efficient Networks
Ming Lin, Hesen Chen, Xiuyu Sun +3
Many mission-critical systems are based on GPU for inference. It requires not only high recognition accuracy but also low latency in responding time. Although many studies are devo…
Towards Understanding Label Smoothing
Yi Xu, Yuanhong Xu, Qi Qian +2
Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants. However, th…
Weakly Supervised Representation Learning with Coarse Labels
Yuanhong Xu, Qi Qian, Hao Li +2
With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most existing algorithms on visual benchmark…
Hierarchically Robust Representation Learning
Qi Qian, Juhua Hu, Hao Li
With the tremendous success of deep learning in visual tasks, the representations extracted from intermediate layers of learned models, that is, deep features, attract much attenti…
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
Qi Qian, Lei Shang, Baigui Sun +3
Distance metric learning (DML) is to learn the embeddings where examples from the same class are closer than examples from different classes. It can be cast as an optimization prob…
DR Loss: Improving Object Detection by Distributional Ranking
Qi Qian, Lei Chen, Hao Li +1
Most of object detection algorithms can be categorized into two classes: two-stage detectors and one-stage detectors. Recently, many efforts have been devoted to one-stage detector…
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
Ming Lin, Xiaomin Song, Qi Qian +4
Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In…
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
Ming Lin, Shuang Qiu, Jieping Ye +5
Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is…