15 citations · 51 across the 16 of their papers we have counts for
16 papers
MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
Jiangwei Weng, Zhiqiang Yan, Ying Tai +3
Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, t…
Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
Junkai Fan, Jiangwei Weng, Kun Wang +4
Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training, especia…
Non-aligned supervision for Real Image Dehazing
Junkai Fan, Fei Guo, Jianjun Qian +3
Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose…
Recurrent Structure Attention Guidance for Depth Super-Resolution
Jiayi Yuan, Haobo Jiang, Xiang Li +3
Image guidance is an effective strategy for depth super-resolution. Generally, most existing methods employ hand-crafted operators to decompose the high-frequency (HF) and low-freq…
Structure Flow-Guided Network for Real Depth Super-Resolution
Jiayi Yuan, Haobo Jiang, Xiang Li +3
Real depth super-resolution (DSR), unlike synthetic settings, is a challenging task due to the structural distortion and the edge noise caused by the natural degradation in real-wo…
Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching
Yikai Bian, Le Hui, Jianjun Qian +1
Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data. Most of existing method…
Generative Subgraph Contrast for Self-Supervised Graph Representation Learning
Yuehui Han, Le Hui, Haobo Jiang +2
Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning meth…
OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation
Jianjun Qian, Shumin Zhu, Chaoyu Zhao +2
Face representation in the wild is extremely hard due to the large scale face variations. To this end, some deep convolutional neural networks (CNNs) have been developed to learn d…
Cross-View Panorama Image Synthesis
Songsong Wu, Hao Tang, Xiao-Yuan Jing +4
In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap betwee…
Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval
Rui Xu, Zongyan Han, Le Hui +2
Sketch-based 3D shape retrieval is a challenging task due to the large domain discrepancy between sketches and 3D shapes. Since existing methods are trained and evaluated on the sa…
Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration
Haobo Jiang, Yaqi Shen, Jin Xie +3
In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsu…
Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration
Haobo Jiang, Jin Xie, Jianjun Qian +1
Point cloud registration is a fundamental problem in 3D computer vision. In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which ca…
Progressive Point Cloud Deconvolution Generation Network
Le Hui, Rui Xu, Jin Xie +2
In this paper, we propose an effective point cloud generation method, which can generate multi-resolution point clouds of the same shape from a latent vector. Specifically, we deve…
Structured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation
Songsong Wu, Yan Yan, Hao Tang +3
Unsupervised Domain Adaptation (UDA) addresses the problem of performance degradation due to domain shift between training and testing sets, which is common in computer vision appl…
DSFD: Dual Shot Face Detector
Jian Li, Yabiao Wang, Changan Wang +6
In this paper, we propose a novel face detection network with three novel contributions that address three key aspects of face detection, including better feature learning, progres…
Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
Jian Yang, Jianjun Qian, Lei Luo +2
Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes t…