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Spatially adaptive pyramid feature fusion for scale-aware crowd counting

Pattern Recognition 2025年 168卷

作者： Shenjian Gong Zhaoliang Yao Wangmeng Zuo Jian Yang PongChi Yuen Shanshan Zhang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Hikvision Research Institute China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Department of Computer Science Hong Kong Baptist University Hong Kong

Crowd counting is a challenging task to predict the number of persons in crowded scenes. The major challenge comes from density/scale variation in one single image, which results in a significant performance decrease for most methods. In this work, we aim to explore how to solve the scale variation problem with assistance from the segmentation task. We first simultaneously predict a coarse density map, along with multi-scale segmentation masks, which provide strong priors of background and head regions with different scales; then we propose a Spatially Adaptive Pyramid (SAP) feature fusion block to fully integrate the coarse density map and multi-scale segmentation masks; in SAP, a feature pyramid and corresponding attention pyramid are obtained for multi-scale head regions, and then cross-scale correlations are computed to handle the scale variation problem. These high-dimensional cross-scale correlation features are then used to refine the density map, based on which the final counting numbers are obtained. Experiments show that our proposed SAP block significantly outperforms previous fusion methods, and obtains consistent improvements on various backbones. Compared to previous state-of-the-art methods, our method is better at handling scale variation, surpassing previous methods on the challenging ShanghaiTech A, UCF-QNRF and NWPU datasets.

关键词： image segmentation

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Generalizing nucleus recognition model in multi-source images via pruning

arXiv

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arXiv 2021年

作者： Cai, Jiatong Zhu, Chenglu Cui, Can Li, Honglin Wu, Tong Zhang, Shichuan Yang, Lin Artificial Intelligence and Biomedical Image Analysis Lab. School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China College of Computer Science & Technology Zhejiang University China

Ki67 is a significant biomarker in the diagnosis and prognosis of cancer, whose index can be evaluated by quantifying its expression in Ki67 immunohistochemistry (IHC) stained images. However, quantitative analysis on multi-source Ki67 images is yet a challenging task in practice due to cross-domain distribution differences, which result from imaging variation, staining styles and lesion types. Many recent studies have made some efforts on domain generalization (DG), whereas there are still some noteworthy limitations. Specifically in the case of Ki67 images, learning invariant representation is at the mercy of the insufficient number of domains and the cell categories mismatching in different domains. In this paper, we propose a novel method to improve DG by searching the domain-agnostic subnetwork in a domain merging scenario. Partial model parameters are iteratively pruned according to the domain gap, which is caused by the data converting from a single domain into merged domains during training. In addition, the model is optimized by fine-tuning on merged domains to eliminate the interference of class mismatching among various domains. Furthermore, an appropriate implementation is attained by applying the pruning method to different parts of the framework. Compared with known DG methods, our method yields excellent performance in multiclass nucleus recognition of Ki67 IHC images, especially in the lost category cases. Moreover, our competitive results are also evaluated on the public dataset over the state-of-the-art DG methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning

arXiv

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arXiv 2022年

作者： Wang, Xumeng Chen, Wei Xia, Jiazhi Wen, Zhen Zhu, Rongchen Schreck, Tobias TMCC CS Nankai University China State Key Lab of CAD&CG Zhejiang University China Laboratory of Art and Archaeology Image Zhejiang University Ministry of Education China School of Computer Science and Engineering Central South University China Graz University of Technology Austria

Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis. Copyright © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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Graph Convolutional Network-based Functional-structural Sub-region Framework for Head and Neck Cancer Prognosis with PET/CT Imaging

Graph Convolutional Network-based Functional-structural Sub-...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Zidong Zhou Junyi Peng Guoyu Lin Huiqin Wu Wenbing Lv Lijun Lu School of Biomedical Engineering Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangdong Provincial Key Laboratory of Medical Image Processing Guangzhou China School of Information Science and Engineering Yunnan University Kunming China Pazhou Lab Guangzhou China

In order to improve the behavior of the prognostic model for patients with head and neck cancer (HNC), we constructed a graph convolutional network (GCN), characterizing the interaction of functional and structural information among different sub-regions of tumors in both PET and CT images. Sub-regions were obtained by using the Watershed-based method and were regarded as vertexes in the graph. The element of 1 in the adjacency matrix indicates corresponding two vertices are spatially contiguous, while the element of 0 indicates they are separated. The selected radiomics features were used to characterize each vertex. 642 HNC patients from 8 different centers were used. The best C-index of 0.767 was achieved by the combination of GCN and clinical parameters compared to the radiomics model (0.747) or GCN model (0.702). the proposed approach has great potential in aiding treatment decision-making.

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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Privileged label enhancement with multi-label learning 29

Privileged label enhancement with multi-label learning

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhu, Wenfang Jia, Xiuyi Li, Weiwei Key Laboratory of Information Perception and Systems for Public Security of MIIT Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security Nanjing University of Science and Technology China State Key Laboratory for Novel Software Technology Nanjing University China College of Astronautics Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9780999241165

label distribution learning has attracted more and more attention in view of its more generalized ability to express the label ambiguity. However, it is much more expensive to obtain the label distribution information of the data rather than the logical labels. Thus, label enhancement is proposed to recover the label distributions from the logical labels. In this paper, we propose a novel label enhancement method by using privileged information. We first apply a multi-label learning model to implicitly capture the complex structural information between instances and generate the privileged information. Second, we adopt LUPI (learning with privileged information) paradigm to utilize the privileged information and employ RSVM+ as the prediction model. Finally, comparison experiments on 12 datasets demonstrate that our proposal can better fit the ground-truth label distributions. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems

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Improved multi-scale dynamic feature encoding network for image demoiréing

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Pattern Recognition 2021年 116卷 107970-107970页

作者： Cheng, Xi Fu, Zhenyong Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

The popularity of smartphones with digital cameras makes photographing using smartphones an important daily activity. Moiré patterns can easily appear when shooting objects with rich textures, such as computer screens, and will severely degrade the image quality. image demoiréing is an important image restoration task that aims to remove moiré patterns and reveal the underlying clean image. Two key properties of moiré patterns—the widely distributed frequency spectrum and the dynamic nature of moiré textures—challenge the image demoiréing task. In this paper, we propose an improved Multi-scale convolutional network with Dynamic feature encoding for image DeMoiréing (MDDM+). We design two schemes in our network to respectively attack the broad frequency spectrum and the dynamic texture of moiré: a multi-scale structure to process images at different spatial resolutions and a dynamic feature encoding module to encode the texture dynamically. To capture more moiré and texture information from different frequencies, we further propose a novel L₁ wavelet loss used to train our model. Extensive experiments on two benchmarks show that our proposed image demoiréing network can outperform the state of the arts in terms of fidelity as well as perception. © 2021

关键词： Dynamic feature encoding image demoiréing Moiré pattern Screen shot images

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Tensor based multi-view label enhancement for multi-label learning 29

Tensor based multi-view label enhancement for multi-label le...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Fangwen Jia, Xiuyi Li, Weiwei Key Laboratory of Information Perception and Systems for Public Security of MIIT Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security Nanjing University of Science and Technology China State Key Laboratory for Novel Software Technology Nanjing University China College of Astronautics Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9780999241165

label enhancement (LE) is a procedure of recovering the label distributions from the logical labels in the multi-label data, the purpose of which is to better represent and mine the label ambiguity problem through the form of label distribution. Existing LE work mainly concentrates on how to leverage the topological information of the feature space and the correlation among the labels, and all are based on single view data. In view of the fact that there are many multi-view data in real-world applications, which can provide richer semantic information from different perspectives, this paper first presents a multi-view label enhancement problem and proposes a tensor-based multi-view label enhancement method, named TMV-LE. Firstly, we introduce the tensor factorization to get the common subspace which contains the high-order relationships among different views. Secondly, we use the common representation and multiple views to jointly mine a more comprehensive topological structure in the dataset. Finally, the topological structure of the feature space is migrated to the label space to get the label distributions. Extensive comparative studies validate that the performance of multi-view multi-label learning can be improved significantly with TMV-LE. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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Partial Differential Equations is All You Need for Generating Neural Architectures - A Theory for Physical Artificial Intelligence Systems

arXiv

引用

arXiv 2021年

作者： Guo, Ping Huang, Kaizhu Xu, Zenglin Image Processing & Pattern Recognition Lab. Beijing Normal University Beijing100875 China Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China School of Computer Science and Technology Harbin Institute of Technology at ShenZhen Peng Cheng National Lab Guangdong Shenzhen510855 China

In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, and Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research. We take the finite difference method to discretize NPDEs for finding the numerical solution. Moreover, the basic building blocks of deep neural network architectures, including multi-layer perceptron, convolutional neural networks, and recurrent neural networks, are generated. The learning strategies, such as Adaptive moment estimation, L-BFGS, pseudoinverse learning algorithms, and partial differential equation constrained optimization, are also developed. This work is of significance in that it presents a clear view of interpretable deep neural networks physically, enables high possibility to be applied to analog computing device design, and paves the road to physical artificial intelligence. © 2021, CC BY-NC-SA.

关键词： Schrodinger equation

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Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians 16th

Do Not Disturb Me: Person Re-identification Under the Interf...

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030585389

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. © 2020, Springer Nature Switzerland AG.

关键词： Computers

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