103 citations · 126 across the 17 of their papers we have counts for
17 papers
Improving Out-of-Distribution Robustness of Classifiers via Generative Interpolation
Haoyue Bai, Ceyuan Yang, Yinghao Xu +2
Deep neural networks achieve superior performance for learning from independent and identically distributed (i.i.d.) data. However, their performance deteriorates significantly whe…
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
Weipeng Zhuo, Ka Ho Chiu, Jierun Chen +4
Floor labels of crowdsourced RF signals are crucial for many smart-city applications, such as multi-floor indoor localization, geofencing, and robot surveillance. To build a predic…
Agent-Based Modelling for Real-World Stock Markets under Behavioral Economic Principles
Tianlang He, Fengming Zhu, Keyan Lu +6
The reproduction of realistic dynamics in financial markets is of great significance, as it enhances our understanding of market evolution beyond other physical processes, and faci…
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
Jierun Chen, Shiu-hong Kao, Hao He +4
To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does no…
Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services
Weipeng Zhuo, Ka Ho Chiu, Jierun Chen +5
In applications such as elderly care, dementia anti-wandering and pandemic control, it is important to ensure that people are within a predefined area for their safety and well-bei…
TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing
Jierun Chen, Tianlang He, Weipeng Zhuo +3
As convolution has empowered many smart applications, dynamic convolution further equips it with the ability to adapt to diverse inputs. However, the static and dynamic convolution…
Tackling Multipath and Biased Training Data for IMU-Assisted BLE Proximity Detection
Tianlang He, Jiajie Tan, Weipeng Zhuo +2
Proximity detection is to determine whether an IoT receiver is within a certain distance from a signal transmitter. Due to its low cost and high popularity, Bluetooth low energy (B…
A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting
Guanyao Li, Shuhan Zhong, S. -H. Gary Chan +3
We study the forecasting problem for traffic with dynamic, possibly periodical, and joint spatial-temporal dependency between regions. Given the aggregated inflow and outflow traff…
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
Haoyue Bai, Fengwei Zhou, Lanqing Hong +3
Recent advances on Out-of-Distribution (OoD) generalization reveal the robustness of deep learning models against distribution shifts. However, existing works focus on OoD algorith…
Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification
Haoyue Bai, Song Wen, S. -H. Gary Chan
Labeled crowd scene images are expensive and scarce. To significantly reduce the requirement of the labeled images, we propose ColorCount, a novel CNN-based approach by combining s…
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting
Haoyue Bai, S. -H. Gary Chan
We study video crowd counting, which is to estimate the number of objects (people in this paper) in all the frames of a video sequence. Previous work on crowd counting is mostly on…
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty
Jierun Chen, Song Wen, S. -H. Gary Chan
Image demosaicking and denoising are the two key fundamental steps in digital camera pipelines, aiming to reconstruct clean color images from noisy luminance readings. In this pape…
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
Haoyue Bai, Rui Sun, Lanqing Hong +5
While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, wh…
A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal
Haoyue Bai, Jiageng Mao, S. -H. Gary Chan
Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc. With the recent development of…
Crowd Counting on Images with Scale Variation and Isolated Clusters
Haoyue Bai, Song Wen, S. -H. Gary Chan
Crowd counting is to estimate the number of objects (e.g., people or vehicles) in an image of unconstrained congested scenes. Designing a general crowd counting algorithm applicabl…
DA-LSTM: A Long Short-Term Memory with Depth Adaptive to Non-uniform Information Flow in Sequential Data
Yifeng Zhang, Ka-Ho Chow, S. -H. Gary Chan
Much sequential data exhibits highly non-uniform information distribution. This cannot be correctly modeled by traditional Long Short-Term Memory (LSTM). To address that, recent wo…
Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder
Ka-Ho Chow, Anish Hiranandani, Yifeng Zhang +1
Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes…