26 citations · 38 across the 10 of their papers we have counts for
11 papers
MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking
Tianhao Li, Tingfa Xu, Ying Wang +3
Drone-based multi-object tracking is essential yet highly challenging due to small targets, severe occlusions, and cluttered backgrounds. Existing RGB-based tracking algorithms hea…
FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking
Ying Wang, Tingfa Xu, Jianan Li
Anti-UAV tracking poses significant challenges, including small target sizes, abrupt camera motion, and cluttered infrared backgrounds. Existing tracking paradigms can be broadly c…
Multi-step Temporal Modeling for UAV Tracking
Xiaoying Yuan, Tingfa Xu, Xincong Liu +4
In the realm of unmanned aerial vehicle (UAV) tracking, Siamese-based approaches have gained traction due to their optimal balance between efficiency and precision. However, UAV sc…
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation
Shenwang Jiang, Jianan Li, Ying Wang +4
Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation. Previou…
BACTrack: Building Appearance Collection for Aerial Tracking
Xincong Liu, Tingfa Xu, Ying Wang +4
Siamese network-based trackers have shown remarkable success in aerial tracking. Most previous works, however, usually perform template matching only between the initial template a…
A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs
Ying Wang, Ye Yuan, Xin Luo
An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a so…
Dynamic Loss For Robust Learning
Shenwang Jiang, Jianan Li, Jizhou Zhang +2
Label noise and class imbalance commonly coexist in real-world data. Previous works for robust learning, however, usually address either one type of the data biases and underperfor…
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
Shenwang Jiang, Jianan Li, Ying Wang +3
Corrupted labels and class imbalance are commonly encountered in practically collected training data, which easily leads to over-fitting of deep neural networks (DNNs). Existing ap…
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds
Jie Wang, Jianan Li, Lihe Ding +2
Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds. Nevertheless, existing…
Pyramid Correlation based Deep Hough Voting for Visual Object Tracking
Ying Wang, Tingfa Xu, Jianan Li +2
Most of the existing Siamese-based trackers treat tracking problem as a parallel task of classification and regression. However, some studies show that the sibling head structure c…
Differentiable Joint Pruning and Quantization for Hardware Efficiency
Ying Wang, Yadong Lu, Tijmen Blankevoort
We present a differentiable joint pruning and quantization (DJPQ) scheme. We frame neural network compression as a joint gradient-based optimization problem, trading off between mo…