167 citations · 383 across the 11 of their papers we have counts for
11 papers
Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction
Dubing Chen, Huan Zheng, Jin Fang +6
We present GDFusion, a temporal fusion method for vision-based 3D semantic occupancy prediction (VisionOcc). GDFusion opens up the underexplored aspects of temporal fusion within t…
Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion
Haoran Wei, Wencheng Han, Xingping Dong +1
Recent diffusion-based Single-image 3D portrait generation methods typically employ 2D diffusion models to provide multi-view knowledge, which is then distilled into 3D representat…
TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision
Yukun Zhai, Xiaoqiang Zhang, Xiameng Qin +3
End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on Regio…
Referring Multi-Object Tracking
Dongming Wu, Wencheng Han, Tiancai Wang +3
Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, ter…
Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving
Jiawei Liu, Xingping Dong, Sanyuan Zhao +1
Recent years have witnessed huge successes in 3D object detection to recognize common objects for autonomous driving (e.g., vehicles and pedestrians). However, most methods rely he…
Adaptive Siamese Tracking with a Compact Latent Network
Xingping Dong, Jianbing Shen, Fatih Porikli +2
In this paper, we provide an intuitive viewing to simplify the Siamese-based trackers by converting the tracking task to a classification. Under this viewing, we perform an in-dept…
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
Xingping Dong, Jianbing Shen, Ling Shao
The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervis…
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning
Xingping Dong, Tianran Ouyang, Shengcai Liao +2
Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit. To reduce the requirement of labels, a semi-supervise…
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
Wencheng Han, Xingping Dong, Fahad Shahbaz Khan +2
Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCor…
Distilled Siamese Networks for Visual Tracking
Jianbing Shen, Yuanpei Liu, Xingping Dong +3
In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from…
Quadruplet Network with One-Shot Learning for Fast Visual Object Tracking
Xingping Dong, Jianbing Shen, Dongming Wu +3
In the same vein of discriminative one-shot learning, Siamese networks allow recognizing an object from a single exemplar with the same class label. However, they do not take advan…