Author name cluster

Qian Ning

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

EAAI Journal 2025 Journal Article

ASTTN: An Adaptive Spatial–Temporal Transformer Network for traffic flow prediction

Zijie Xue
Linyu Huang
Qian Ning

Traffic flow prediction as a representative challenge in spatial–temporal modeling tasks, playing a fundamental role in intelligent transportation systems (ITSs). The complexity of spatial–temporal features from road networks and unexpected events complicates accurate predictions. To address the above problems, we propose a novel Adaptive Spatial–Temporal Transformer Network (ASTTN) designed to effectively capture dynamic spatial–temporal features and improve the model’s predictive adaptability under different systems. Considering the comprehensive temporal features, we design a Multi-View Temporal Attention Module to extract long-term and short-term temporal correlation. Additionally, by the limitation of the fixed adjacency matrix, our model proposes an Adaptive Spatial Graph Convolutional Network Module to dynamically capture spatial dependence by the adaptive mechanism based on self-attention. An adaptive Gated Fusion module is employed to achieve the dynamic fusion of temporal and spatial features. Experiments on four real-world traffic datasets show that our model significantly outperforms existing state-of-the-art baselines.

Details DOI

IJCAI Conference 2022 Conference Paper

Learning Degradation Uncertainty for Unsupervised Real-world Image Super-resolution

Qian Ning
Jingzhu Tang
Fangfang Wu
Weisheng Dong
Xin Li
Guangming Shi

Acquiring degraded images with paired high-resolution (HR) images is often challenging, impeding the advance of image super-resolution in real-world applications. By generating realistic low-resolution (LR) images with degradation similar to that in real-world scenarios, simulated paired LR-HR data can be constructed for supervised training. However, most of the existing work ignores the degradation uncertainty of the generated realistic LR images, since only one LR image has been generated given an HR image. To address this weakness, we propose learning the degradation uncertainty of generated LR images and sampling multiple LR images from the learned LR image (mean) and degradation uncertainty (variance) and construct LR-HR pairs to train the super-resolution (SR) networks. Specifically, uncertainty can be learned by minimizing the proposed loss based on Kullback-Leibler (KL) divergence. Furthermore, the uncertainty in the feature domain is exploited by a novel perceptual loss; and we propose to calculate the adversarial loss from the gradient information in the SR stage for stable training performance and better visual quality. Experimental results on popular real-world datasets show that our proposed method has performed better than other unsupervised approaches.

PDF Details DOI

NeurIPS Conference 2021 Conference Paper

Uncertainty-Driven Loss for Single Image Super-Resolution

Qian Ning
Weisheng Dong
Xin Li
Jinjian Wu
Guangming Shi

In low-level vision such as single image super-resolution (SISR), traditional MSE or L 1 loss function treats every pixel equally with the assumption that the importance of all pixels is the same. However, it has been long recognized that texture and edge areas carry more important visual information than smooth areas in photographic images. How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty. Specifically, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously. Moreover, uncertainty estimation allows us to leverage conventional wisdom such as sparsity prior for regularizing SISR solutions. Ultimately, pixels with large certainty (e. g. , texture and edge pixels) will be prioritized for SISR according to their importance to visual quality. For the first time, we demonstrate that such uncertainty-driven loss can achieve better results than MSE or L 1 loss for a wide range of network architectures. Experimental results on three popular SISR networks show that our proposed uncertainty-driven loss has achieved better PSNR performance than traditional loss functions without any increased computation during testing. The code is available at https: //see. xidian. edu. cn/faculty/wsdong/Projects/UDL-SR. htm

PDF Details

AAAI Conference 2020 Conference Paper

Spatial-Temporal Gaussian Scale Mixture Modeling for Foreground Estimation

Qian Ning
Weisheng Dong
Fangfang Wu
Jinjian Wu
Jie Lin
Guangming Shi

Subtracting the backgrounds from the video frames is an important step for many video analysis applications. Assuming that the backgrounds are low-rank and the foregrounds are sparse, the robust principle component analysis (RPCA)based methods have shown promising results. However, the RPCA-based methods suffered from the scale issue, i. e. , the 1-sparsity regularizer fails to model the varying sparsity of the moving objects. While several efforts have been made to address this issue with advanced sparse models, previous methods cannot fully exploit the spatial-temporal correlations among the foregrounds. In this paper, we proposed a novel spatial-temporal Gaussian scale mixture (STGSM) model for foreground estimation. In the proposed STGSM model, a temporal consistent constraint is imposed over the estimated foregrounds through nonzero-means Gaussian models. Speciﬁcally, the estimates of the foregrounds obtained in the previous frame are used as the prior for these of the current frame, and nonzero means Gaussian scale mixture models (GSM) are developed. To better characterize the temporal correlations, the optical ﬂow has been used to model the correspondences between foreground pixels in adjacent frames. The spatial correlations have also been exploited by considering that local correlated pixels should be characterized by the same STGSM model, leading to further performance improvements. Experimental results on real video datasets show that the proposed method performs comparably or even better than current state-of-the-art background subtraction methods.

PDF Details