116 citations · 302 across the 19 of their papers we have counts for
20 papers
TDViT: Temporal Dilated Video Transformer for Dense Video Tasks
Guanxiong Sun, Yang Hua, Guosheng Hu +1
Deep video models, for example, 3D CNNs or video transformers, have achieved promising performance on sparse video tasks, i.e., predicting one result per video. However, challenges…
Efficient One-stage Video Object Detection by Exploiting Temporal Consistency
Guanxiong Sun, Yang Hua, Guosheng Hu +1
Recently, one-stage detectors have achieved competitive accuracy and faster speed compared with traditional two-stage detectors on image data. However, in the field of video object…
MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection
Guanxiong Sun, Yang Hua, Guosheng Hu +1
State-of-the-art video object detection methods maintain a memory structure, either a sliding window or a memory queue, to enhance the current frame using attention mechanisms. How…
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Hao Chen, Ran Tao, Han Zhang +6
While parameter efficient tuning (PET) methods have shown great potential with transformer architecture on Natural Language Processing (NLP) tasks, their effectiveness with large-s…
Boosting Active Learning via Improving Test Performance
Tianyang Wang, Xingjian Li, Pengkun Yang +5
Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless,…
DPT: Deformable Patch-based Transformer for Visual Recognition
Zhiyang Chen, Yousong Zhu, Chaoyang Zhao +4
Transformer has achieved great success in computer vision, while how to split patches in an image remains a problem. Existing methods usually use a fixed-size patch embedding which…
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
Tingting Liang, Yongtao Wang, Zhi Tang +2
Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by th…
Imbalance Robust Softmax for Deep Embeeding Learning
Hao Zhu, Yang Yuan, Guosheng Hu +2
Deep embedding learning is expected to learn a metric space in which features have smaller maximal intra-class distance than minimal inter-class distance. In recent years, one rese…
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision
Yuxiang Wei, Ming Liu, Haolin Wang +3
Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pos…
Salvage Reusable Samples from Noisy Data for Robust Learning
Zeren Sun, Xian-Sheng Hua, Yazhou Yao +3
Due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images ten…
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning
Zhijun Mai, Guosheng Hu, Dexiong Chen +2
MixUp is an effective data augmentation method to regularize deep neural networks via random linear interpolations between pairs of samples and their labels. It plays an important…
Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection
Zhiwei Liu, Xiangyu Zhu, Guosheng Hu +5
Recently, deep learning based facial landmark detection has achieved great success. Despite this, we notice that the semantic ambiguity greatly degrades the detection performance.…
Ranked List Loss for Deep Metric Learning
Xinshao Wang, Yang Hua, Elyor Kodirov +1
The objective of deep metric learning (DML) is to learn embeddings that can capture semantic similarity and dissimilarity information among data points. Existing pairwise or triple…
Learning Symmetry Consistent Deep CNNs for Face Completion
Xiaoming Li, Ming Liu, Jieru Zhu +4
Deep convolutional networks (CNNs) have achieved great success in face completion to generate plausible facial structures. These methods, however, are limited in maintaining global…
Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Xinshao Wang, Yang Hua, Elyor Kodirov +2
Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learnin…
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
Guosheng Hu, Yuxin Hu, Kai Yang +8
We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measureme…
Dictionary Integration using 3D Morphable Face Models for Pose-invariant Collaborative-representation-based Classification
Xiaoning Song, Zhen-Hua Feng, Guosheng Hu +3
The paper presents a dictionary integration algorithm using 3D morphable face models (3DMM) for pose-invariant collaborative-representation-based face classification. To this end,…
Frankenstein: Learning Deep Face Representations using Small Data
Guosheng Hu, Xiaojiang Peng, Yongxin Yang +2
Deep convolutional neural networks have recently proven extremely effective for difficult face recognition problems in uncontrolled settings. To train such networks, very large tra…
When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition
Guosheng Hu, Yongxin Yang, Dong Yi +4
Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work we…
Identifying Similar Patients Using Self-Organising Maps: A Case Study on Type-1 Diabetes Self-care Survey Responses
Santosh Tirunagari, Norman Poh, Guosheng Hu +1
Diabetes is considered a lifestyle disease and a well managed self-care plays an important role in the treatment. Clinicians often conduct surveys to understand the self-care behav…