122 citations · 572 across the 79 of their papers we have counts for
14 papers · 1 filter
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
Rui Li, Qing Mao, Pei Wang +4
Self-supervised depth estimation has shown its great effectiveness in producing high quality depth maps given only image sequences as input. However, its performance usually drops…
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution
Jiangtao Nie, Lei Zhang, Wei Wei +2
Despite the great success of deep model on Hyperspectral imagery (HSI) super-resolution(SR) for simulated data, most of them function unsatisfactory when applied to the real data,…
Meta-Generating Deep Attentive Metric for Few-shot Classification
Lei Zhang, Fei Zhou, Wei Wei +1
Learning to generate a task-aware base learner proves a promising direction to deal with few-shot learning (FSL) problem. Existing methods mainly focus on generating an embedding m…
On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration
Jiaqi Yang, Zhiqiang Huang, Siwen Quan +3
This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) p…
Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network
Haibo Su, Peng Wang, Lingqiao Liu +3
Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extr…
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization
Congqi Cao, Yajuan Li, Qinyi Lv +2
Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application. Although there has been a lot of work…
AE-Netv2: Optimization of Image Fusion Efficiency and Network Architecture
Aiqing Fang, Xinbo Zhao, Jiaqi Yang +2
Existing image fusion methods pay few research attention to image fusion efficiency and network architecture. However, the efficiency and accuracy of image fusion has an important…
3D Correspondence Grouping with Compatibility Features
Jiaqi Yang, Jiahao Chen, Zhiqiang Huang +3
We present a simple yet effective method for 3D correspondence grouping. The objective is to accurately classify initial correspondences obtained by matching local geometric descri…
AE-Net: Autonomous Evolution Image Fusion Method Inspired by Human Cognitive Mechanism
Aiqing Fang, Xinbo Zhao, Jiaqi Yang +2
In order to solve the robustness and generality problems of the image fusion task,inspired by the human brain cognitive mechanism, we propose a robust and general image fusion meth…
IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality
Di Xu, Zhen Li, Yanning Zhang +1
This paper presents an illumination estimation method for virtual objects in real environment by learning. While previous works tackled this problem by reconstructing high dynamic…
A Robust Attentional Framework for License Plate Recognition in the Wild
Linjiang Zhang, Peng Wang, Hui Li +3
Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plate…
Attention-based network for low-light image enhancement
Cheng Zhang, Qingsen Yan, Yu zhu +3
The captured images under low light conditions often suffer insufficient brightness and notorious noise. Hence, low-light image enhancement is a key challenging task in computer vi…
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning
Congqi Cao, Yanning Zhang
Few-shot learning is a fundamental and challenging problem since it requires recognizing novel categories from only a few examples. The objects for recognition have multiple varian…
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
Dong Gong, Wei Sun, Qinfeng Shi +2
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods…