60 citations · 151 across the 17 of their papers we have counts for
17 papers
SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement
Hanting Li, Fei Zhou, Xin Sun +3
Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination. However, they still struggle with non-uniform lighting scenar…
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…
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…
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
Xinshao Wang, Yang Hua, Elyor Kodirov +3
There is a family of label modification approaches including self and non-self label correction (LC), and output regularisation. They are widely used for training robust deep neura…
Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques
JunKyu Lee, Lev Mukhanov, Amir Sabbagh Molahosseini +6
Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep ne…
Fast and Accurate Scene Parsing via Bi-direction Alignment Networks
Yanran Wu, Xiangtai Li, Chen Shi +5
In this paper, we propose an effective method for fast and accurate scene parsing called Bidirectional Alignment Network (BiAlignNet). Previously, one representative work BiSeNet~\…
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation
Shiyang Yan, Yang Hua, Neil M. Robertson
Recently, several approaches have been proposed to solve language generation problems. Transformer is currently state-of-the-art seq-to-seq model in language generation. Reinforcem…
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
Xinshao Wang, Yang Hua, Elyor Kodirov +2
To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction…
ParaCNN: Visual Paragraph Generation via Adversarial Twin Contextual CNNs
Shiyang Yan, Yang Hua, Neil Robertson
Image description generation plays an important role in many real-world applications, such as image retrieval, automatic navigation, and disabled people support. A well-developed t…
Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation
Yihan Du, Yan Yan, Si Chen +1
In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs…
Instance Cross Entropy for Deep Metric Learning
Xinshao Wang, Elyor Kodirov, Yang Hua +1
Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity cons…
ID-aware Quality for Set-based Person Re-identification
Xinshao Wang, Elyor Kodirov, Yang Hua +1
Set-based person re-identification (SReID) is a matching problem that aims to verify whether two sets are of the same identity (ID). Existing SReID models typically generate a feat…
Derivative Manipulation for General Example Weighting
Xinshao Wang, Elyor Kodirov, Yang Hua +1
Real-world large-scale datasets usually contain noisy labels and are imbalanced. Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach…
GAN-based Pose-aware Regulation for Video-based Person Re-identification
Alessandro Borgia, Yang Hua, Elyor Kodirov +1
Video-based person re-identification deals with the inherent difficulty of matching unregulated sequences with different length and with incomplete target pose/viewpoint structure.…
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…
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…
Online Object Tracking with Proposal Selection
Yang Hua, Karteek Alahari, Cordelia Schmid
Tracking-by-detection approaches are some of the most successful object trackers in recent years. Their success is largely determined by the detector model they learn initially and…