39 citations · 102 across the 21 of their papers we have counts for
22 papers
Efficient RWKV-based Representation Learning for 3D Point Clouds
Yun Liu, Xuefeng Yan, Liangliang Nan +5
The recent receptance weighted key value (RWKV) model combines RNN-style recurrence, offering a linear-complexity alternative to Transformers' quadratic self-attention for modeling…
An efficient deep reinforcement learning environment for flexible job-shop scheduling
Xinquan Wu, Xuefeng Yan, Mingqiang Wei +1
The Flexible Job-shop Scheduling Problem (FJSP) is a classical combinatorial optimization problem that has a wide-range of applications in the real world. In order to generate fast…
DA2Diff: Exploring Degradation-aware Adaptive Diffusion Priors for All-in-One Weather Restoration
Jiamei Xiong, Xuefeng Yan, Yongzhen Wang +3
Image restoration under adverse weather conditions is a critical task for many vision-based applications. Recent all-in-one frameworks that handle multiple weather degradations wit…
CrossTracker: Robust Multi-modal 3D Multi-Object Tracking via Cross Correction
Lipeng Gu, Xuefeng Yan, Weiming Wang +4
The fusion of camera- and LiDAR-based detections offers a promising solution to mitigate tracking failures in 3D multi-object tracking (MOT). However, existing methods predominantl…
PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation
Yun Liu, Peng Li, Xuefeng Yan +6
The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects…
RegTrack: Simplicity Beneath Complexity in Robust Multi-Modal 3D Multi-Object Tracking
Lipeng Gu, Xuefeng Yan, Song Wang +1
Existing 3D multi-object tracking (MOT) methods often sacrifice efficiency and generalizability for robustness, largely relying on complex association metrics derived from multi-mo…
RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image Dehazing
Jiamei Xiong, Xuefeng Yan, Yongzhen Wang +3
Haze severely degrades the visual quality of remote sensing images and hampers the performance of road extraction, vehicle detection, and traffic flow monitoring. The emerging deno…
PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis
Lipeng Gu, Xuefeng Yan, Liangliang Nan +4
Current methodologies in point cloud analysis predominantly explore 3D geometries, often achieved through the introduction of intricate learnable geometric extractors in the encode…
Uncertainty-Driven Multi-Scale Feature Fusion Network for Real-time Image Deraining
Ming Tong, Xuefeng Yan, Yongzhen Wang
Visual-based measurement systems are frequently affected by rainy weather due to the degradation caused by rain streaks in captured images, and existing imaging devices struggle to…
Joint Depth Estimation and Mixture of Rain Removal From a Single Image
Yongzhen Wang, Xuefeng Yan, Yanbiao Niu +3
Rainy weather significantly deteriorates the visibility of scene objects, particularly when images are captured through outdoor camera lenses or windshields. Through careful observ…
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds
Yun Liu, Xuefeng Yan, Zhilei Chen +3
Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to th…
PointSee: Image Enhances Point Cloud
Lipeng Gu, Xuefeng Yan, Peng Cui +5
There is a trend to fuse multi-modal information for 3D object detection (3OD). However, the challenging problems of low lightweightness, poor flexibility of plug-and-play, and ina…
Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing
Ming Tong, Yongzhen Wang, Peng Cui +2
Image dehazing is fundamental yet not well-solved in computer vision. Most cutting-edge models are trained in synthetic data, leading to the poor performance on real-world hazy sce…
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Yongzhen Wang, Xuefeng Yan, Kaiwen Zhang +4
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in the…
Contrastive Semantic-Guided Image Smoothing Network
Jie Wang, Yongzhen Wang, Yidan Feng +5
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in…
UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
Zhilei Chen, Honghua Chen, Lina Gong +5
High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner. However, there inh…
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Peng Wu, Lipeng Gu, Xuefeng Yan +4
Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing…
SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
Haoran Pan, Jun Zhou, Yuanpeng Liu +4
6D pose estimation of rigid objects from RGB-D images is crucial for object grasping and manipulation in robotics. Although RGB channels and the depth (D) channel are often complem…
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Chen Chen, Yisen Wang, Honghua Chen +6
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most…
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
Qiqi Ding, Peng Li, Xuefeng Yan +6
Snow is one of the toughest adverse weather conditions for object detection (OD). Currently, not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also…
AGConv: Adaptive Graph Convolution on 3D Point Clouds
Mingqiang Wei, Zeyong Wei, Haoran Zhou +9
Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences ind…
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
Yongzhen Wang, Xuefeng Yan, Fu Lee Wang +4
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known d…