1 code implementation • 26 Oct 2024 • Jialin Luo, Yuanzhi Wang, Ziqi Gu, Yide Qiu, Shuaizhen Yao, Fuyun Wang, Chunyan Xu, Wenhua Zhang, Dan Wang, Zhen Cui
However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes.
1 code implementation • 3 Sep 2024 • Yanguang Sun, Chunyan Xu, Jian Yang, Hanyu Xuan, Lei Luo
Camouflaged object detection has attracted a lot of attention in computer vision.
1 code implementation • 8 Jul 2024 • Chenxu Wang, Chunyan Xu, Ziqi Gu, Zhen Cui
We experimentally find three gaps between general and oriented object detection in semi-supervised learning: 1) Sampling inconsistency: the common center sampling is not suitable for oriented objects with larger aspect ratios when selecting positive labels from labeled data.
no code implementations • ICCV 2023 • Ziqi Gu, Chunyan Xu, Jian Yang, Zhen Cui
Further, considering that the learned knowledge in the human brain is a generalization of actual information and exists in a certain relational structure, we perform continual structure infomax learning to relieve the catastrophic forgetting problem in the continual learning process.
1 code implementation • CVPR 2022 • Ziqiang Xu, Chunyan Xu, Zhen Cui, Xiangwei Zheng, Jian Yang
The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently.
no code implementations • 14 Apr 2021 • Hui Lv, Chunyan Xu, Zhen Cui
Video anomaly detection (VAD) is currently a challenging task due to the complexity of anomaly as well as the lack of labor-intensive temporal annotations.
1 code implementation • CVPR 2021 • Hui Lv, Chen Chen, Zhen Cui, Chunyan Xu, Yong Li, Jian Yang
Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection.
no code implementations • 10 Mar 2021 • Xuran Xu, Tong Zhang, Chunyan Xu, Zhen Cui, Jian Yang
We further extend graph convolution into tensor space and propose a tensor graph convolution network to extract more discriminating features from spatial-temporal graph data.
Ranked #1 on
Traffic Prediction
on SZ-Taxi
no code implementations • ICCV 2021 • Jingshan Xu, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jian Yang
In this paper, we propose a progressive segmentation inference (PSI) framework to tackle with scribble-supervised semantic segmentation.
no code implementations • 30 Sep 2020 • Zhenzhen Wang, Chunyan Xu, Yap-Peng Tan, Junsong Yuan
In this paper, the attention-aware noisy label learning approach ($A^2NL$) is proposed to improve the discriminative capability of the network trained on datasets with potential label noise.
no code implementations • 3 Sep 2020 • Yuan Fang, Chunyan Xu, Zhen Cui, Yuan Zong, Jian Yang
In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds.
1 code implementation • 20 Aug 2020 • Hui Lv, Chuanwei Zhou, Chunyan Xu, Zhen Cui, Jian Yang
In addition, in order to fully utilize the spatial context information, the immediate semantics are directly derived from the segment representations.
Anomaly Detection In Surveillance Videos
Anomaly Localization
+1
no code implementations • 19 Aug 2020 • Yun Wang, Tong Zhang, Zhen Cui, Chunyan Xu, Jian Yang
For label diffusion of instance-awareness in graph convolution, rather than using the statistical label correlation alone, an image-dependent label correlation matrix (LCM), fusing both the statistical LCM and an individual one of each image instance, is constructed for graph inference on labels to inject adaptive information of label-awareness into the learned features of the model.
no code implementations • ICLR 2020 • Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu
In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures.
no code implementations • CVPR 2019 • Zhen-Yu Zhang, Zhen Cui, Chunyan Xu, Yan Yan, Nicu Sebe, Jian Yang
In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly predict depth, surface normal and semantic segmentation.
Ranked #56 on
Monocular Depth Estimation
on NYU-Depth V2
(RMSE metric)
no code implementations • 11 Nov 2018 • Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang
In this work, we propose a Gaussian-induced convolution (GIC) framework to conduct local convolution filtering on irregular graphs.
Ranked #3 on
Graph Classification
on PTC
no code implementations • 11 Sep 2018 • Zhen Cui, Chunyan Xu, Wenming Zheng, Jian Yang
Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images.
no code implementations • ECCV 2018 • Zhen-Yu Zhang, Zhen Cui, Chunyan Xu, Zequn Jie, Xiang Li, Jian Yang
In this paper, we propose a novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks.
Ranked #82 on
Semantic Segmentation
on NYU Depth v2
no code implementations • 7 Jul 2018 • Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhen-Yu Zhang, Jian Yang
In this paper, we aim to give a comprehensive analysis of when work matters by transforming different classical network structures to graph CNN, particularly in the basic graph recognition problem.
Ranked #4 on
Graph Classification
on COLLAB
no code implementations • 16 Apr 2018 • Jiatao Jiang, Chunyan Xu, Zhen Cui, Tong Zhang, Wenming Zheng, Jian Yang
As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters.
no code implementations • 27 Feb 2018 • Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Jian Yang
To encode dynamic graphs, the constructed multi-scale local graph convolution filters, consisting of matrices of local receptive fields and signal mappings, are recursively performed on structured graph data of temporal and spatial domain.
Ranked #1 on
Skeleton Based Action Recognition
on Florence 3D
no code implementations • 17 Nov 2017 • Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Rongrong Ji, Jian Yang
The motion analysis of human skeletons is crucial for human action recognition, which is one of the most active topics in computer vision.
2 code implementations • ICCV 2017 • Ying Tai, Jian Yang, Xiaoming Liu, Chunyan Xu
We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.
1 code implementation • 22 Dec 2015 • Xiaojie Jin, Chunyan Xu, Jiashi Feng, Yunchao Wei, Junjun Xiong, Shuicheng Yan
Rectified linear activation units are important components for state-of-the-art deep convolutional networks.
no code implementations • ICCV 2015 • Xiaodan Liang, Chunyan Xu, Xiaohui Shen, Jianchao Yang, Si Liu, Jinhui Tang, Liang Lin, Shuicheng Yan
In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network.
no code implementations • 30 Oct 2015 • Hanjiang Lai, Shengtao Xiao, Yan Pan, Zhen Cui, Jiashi Feng, Chunyan Xu, Jian Yin, Shuicheng Yan
We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.
no code implementations • 6 Dec 2014 • Canyi Lu, Changbo Zhu, Chunyan Xu, Shuicheng Yan, Zhouchen Lin
This work studies the Generalized Singular Value Thresholding (GSVT) operator ${\text{Prox}}_{g}^{{\sigma}}(\cdot)$, \begin{equation*} {\text{Prox}}_{g}^{{\sigma}}(B)=\arg\min\limits_{X}\sum_{i=1}^{m}g(\sigma_{i}(X)) + \frac{1}{2}||X-B||_{F}^{2}, \end{equation*} associated with a nonconvex function $g$ defined on the singular values of $X$.