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TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers
Tai An;Xin Zhang;Chunlei Huo;Bin Xue;Lingfeng Wang;Chunhong Pan
Multiimage super-resolution (MISR), as one of the most promising directions in remote sensing, has become a needy technique in the satellite market. A sequence of images collected by satellites often has plenty of views and a long time span, so integrating multiple low-resolution views into a high-resolution image with details emerges as a challenging problem. However, most MISR methods based on d...Show More
Few-Shot Learning via Feature Hallucination with Variational Inference
Qinxuan Luo;Lingfeng Wang;Jingguo Lv;Shiming Xiang;Chunhong Pan
2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2021 | Conference Paper |
Cited by: Papers (29)
Deep learning has achieved huge success in the field of artificial intelligence, but the performance heavily depends on labeled data. Few-shot learning aims to make a model rapidly adapt to unseen classes with few labeled samples after training on a base dataset, and this is useful for tasks lacking labeled data such as medical image processing. Considering that the core problem of few-shot learni...Show More
Ltaf-Net: Learning Task-Aware Adaptive Features and Refining Mask for Few-Shot Semantic Segmentation
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Few shot segmentation is a newly-developing and challenging computer vision task which is only provided with few labeled samples of the novel class. Some recent works on this problem focus more on how to design an effective comparison module but ignore how to extract the features passed to compare. In this paper we propose a novel model named LTAF-Net for few-shot segmentation. This model aims to ...Show More
Local-Aggregation Graph Networks
Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Qi Zhang;Shiming Xiang;Chunhong Pan
Convolutional neural networks (CNNs) provide a dramatically powerful class of models, but are subject to traditional convolution that can merely aggregate permutation-ordered and dimension-equal local inputs. It causes that CNNs are allowed to only manage signals on Euclidean or grid-like domains (e.g., images), not ones on non-Euclidean or graph domains (e.g., traffic networks). To eliminate this...Show More
Deep Self-Evolution Clustering
Jianlong Chang;Gaofeng Meng;Lingfeng Wang;Shiming Xiang;Chunhong Pan
Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is ...Show More
Deep Hierarchical Encoder–Decoder Network for Image Captioning
Xinyu Xiao;Lingfeng Wang;Kun Ding;Shiming Xiang;Chunhong Pan
IEEE Transactions on Multimedia
Cited by: Papers (73)
Encoder-decoder models have been widely used in image captioning, and most of them are designed via single long short term memory (LSTM). The capacity of single-layer network, whose encoder and decoder are integrated together, is limited for such a complex task of image captioning. Moreover, how to effectively increase the “vertical depth” of encoder-decoder remains to be solved. To deal with thes...Show More
Weakly Semantic Guided Action Recognition
Tingzhao Yu;Lingfeng Wang;Cheng Da;Huxiang Gu;Shiming Xiang;Chunhong Pan
IEEE Transactions on Multimedia
Cited by: Papers (26)
Action recognition plays a fundamental role in computer vision and video analysis. Nevertheless, extracting effective spatial-temporal features remains a challenging task. This paper proposes three simple but effective weakly semantic guided modules (SGMs) for both environment-constrained and cross-domain action recognition. The SGMs are composed of total 3-D convolution and element-wise gated ope...Show More
Adaptation of Alpha-Beta Search Algorithm for Real-Time Strategy Game
2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Real-time strategy(RTS)[1] game is known for its large action space, rapid response speed, and subtle game scenarios, and it has proven to be a very challenging application area in artificial intelligence research. In the RTS game, the search time increases exponentially as the number of units increases. So it is not possible to achieve a complete search for the game tree in the strict circumstanc...Show More
Fast Variational Level Set Based Image Segmentation via Two-Scale Filtering Model
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
One major difficulty in medical image segmentation is intensity inhomogeneity, which manifests itself with a slow intensity variation over the whole image domain. Recently, a local binary fitting (LBF) model has been proposed to solve this problem within level set segmentation framework. However, the LBF model has two main problems, i.e., high computational cost and sensitivity to initialization. ...Show More
Reconstructed Densenets for Image Super-Resolution
2018 25th IEEE International Conference on Image Processing (ICIP)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Deep learning has been successfully applied to single image super-resolution problem due to its high data fitting ability. However, the trending of deeper layers and wider receptive field to acquire better performance brings high computation complexity and serious information vanishing. To address this problem, we proposed a new Reconstructed DenseNets model for super-resolution. The basic idea be...Show More
Two-Stream Designed 2D/3D Residual Networks with Lstms for Action Recognition in Videos
2018 25th IEEE International Conference on Image Processing (ICIP)
Year: 2018 | Conference Paper |
Cited by: Papers (9)
Convolutional Neural Networks(CNNs) have achieved great success for object recognition in still images. However, CNNs can't make evident improvement for action recognition in videos, one reason is that many current network architectures are relatively shallow compared with deep models in image domain, and the other reason is that CNNs can't capture effective long-term motion information from video...Show More
Self-Paced AutoEncoder
Tingzhao Yu;Chaoxu Guo;Lingfeng Wang;Shiming Xiang;Chunhong Pan
IEEE Signal Processing Letters
Cited by: Papers (18)
Autoencoder, which learns latent representations of samples in an unsupervised manner, has great potential in computer vision and signal processing. However, the diversity of samples makes learning a component autoencoder remaining a challenging task. This letter proposes a novel Self-Paced AutoEncoder (SPAE) for unsupervised feature extraction. The motivation behind this letter is to take samples...Show More
Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems
Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Shiming Xiang;Chunhong Pan
Due to the factors such as visual occlusion, illumination change and pose variation, it is a challenging task to develop effective and efficient models for vehicle detection and classification in surveillance videos. Although plenty of existing related models have been proposed, many issues still need to be resolved. Typically, vehicle detection and classification methods should be vulnerable in c...Show More
Groupwise Retargeted Least-Squares Regression
In this brief, we propose a new groupwise retargeted least squares regression (GReLSR) model for multicategory classification. The main motivation behind GReLSR is to utilize an additional regularization to restrict the translation values of ReLSR, so that they should be similar within same class. By analyzing the regression targets of ReLSR, we propose a new formulation of ReLSR, where the transl...Show More
Learnable contextual regularization for semantic segmentation of indoor scene images
2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Semantic segmentation of indoor scene images has a wide range of applications. However, due to a large number of classes and uneven distribution in indoor scenes, mislabels are often made when facing small objects or boundary regions. Technically, contextual information may benefit for segmentation results, but has not yet been exploited sufficiently. In this paper, we propose a learnable contextu...Show More
Cascaded temporal spatial features for video action recognition
Tingzhao Yu;Huxiang Gu;Lingfeng Wang;Shiming Xiang;Chunhong Pan
2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Cited by: Papers (8)
Extracting spatial-temporal descriptors is a challenging task for video-based human action recognition. We decouple the 3D volume of video frames directly into a cascaded temporal spatial domain via a new convolutional architecture. The motivation behind this design is to achieve deep nonlinear feature representations with reduced network parameters. First, a 1D temporal network with shared parame...Show More
Image super-resolution via deep dilated convolutional networks
2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Cited by: Papers (8)
Deep learning techniques have been successfully applied in single image super-resolution (SR). Recently, researches have shown that increasing the depth of network can significantly improve SR performance. Very deep networks for SR achieved a large improvement than former methods. However, simply increasing depths basically introduce more parameters and this lead to cumbersome computational cost. ...Show More
Context-aware cascade network for semantic labeling in VHR image
Yongcheng Liu;Bin Fan;Lingfeng Wang;Jun Bai;Shiming Xiang;Chunhong Pan
2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
Semantic labeling for the very high resolution (VHR) image of urban areas is challenging, because of many complex manmade objects with different materials and fine-structured objects located together. Under the framework of convolutional neural networks (CNNs), this paper proposes a novel end-to-end network for semantic labeling. Specifically, our network not only improves the labeling accuracy of...Show More
MR images segmentation and bias correction via LIC model
2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
This paper presents a novel Linear Intrinsic Component (LIC) model for simultaneous estimation of bias field and segmentation of magnetic resonance (MR) images with the intensity inhomogene-ity. The core of LIC model is linear transformation, which is derived from Taylor expansion of non-linear model. Due to the linear transformation, observed image can be decomposed into four components, namely, ...Show More
Deep Adaptive Image Clustering
Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Shiming Xiang;Chunhong Pan
2017 IEEE International Conference on Computer Vision (ICCV)
Year: 2017 | Conference Paper |
Cited by: Papers (271)
Image clustering is a crucial but challenging task in machine learning and computer vision. Existing methods often ignore the combination between feature learning and clustering. To tackle this problem, we propose Deep Adaptive Clustering (DAC) that recasts the clustering problem into a binary pairwise-classification framework to judge whether pairs of images belong to the same clusters. In DAC, t...Show More
RoDLSR: Robust discriminative least squares regression model for multi-category classification
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2017 | Conference Paper |
Cited by: Papers (7)
Discriminative least squares regression (DLSR) is a simple yet effective method for multi-class classification. One problem of DLSR is that it is lack of robustness to outliers. In order to tackle this difficulty, in this paper, we propose a novel Robust DLSR (RoDLSR) model. The core idea behind RoDLSR is to find and further ignore the outliers among the support vector set. Specifically, we modify...Show More
Learning deep vector regression model for no-reference image quality assessment
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
The goal of no-reference image quality assessment (NR-IQA) is to estimate human perceived image quality without access to either reference image or prior knowledge about distortion type. Previous approaches for this problem are typically based on a regression framework that maps the image features directly to a quality score. In contrast, psychological evidence shows that humans prefer to evaluate...Show More
Fine-structured object segmentation via edge-guided graph cut with interaction simplification
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2016 | Conference Paper |
Cited by: Papers (2)
Fine-structured object segmentation is a challenging problem in object segmentation community. There are mainly two difficulties that can seriously degrade the segmentation quality: 1) insufficient interactions on fine structures due to the high demand of time and manual efforts, and 2) shrinking bias that discourages long object boundaries. To address these two issues, we develop a novel method w...Show More
Layer-Wise Floorplan Extraction for Automatic Urban Building Reconstruction
Wei Sui;Lingfeng Wang;Bin Fan;Hongfei Xiao;Huaiyu Wu;Chunhong Pan
Urban building reconstruction is an important step for urban digitization and realisticvisualization. In this paper, we propose a novel automatic method to recover urban building geometry from 3D point clouds. The proposed method is suitable for buildings composed of planar polygons and aligned with the gravity direction, which are quite common in the city. Our key observation is that the building...Show More
MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification
In this brief, we propose a new margin scalable discriminative least squares regression (MSDLSR) model for multicategory classification. The main motivation behind the MSDLSR is to explicitly control the margin of DLSR model. We first prove that the DLSR is a relaxation of the traditional L2-support vector machine. Based on this fact, we further provide a theorem on the margin of DLSR. With this t...Show More
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