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Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention

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arXiv 2024年

作者： Dai, Yimian Pan, Peiwen Qian, Yulei Li, Yuxuan Li, Xiang Yang, Jian Wang, Huan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security China The department of intelligence science School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Nanjing Marine Radar Institute Nanjing China VCIP CS Nankai University China The NKIARI Futian Shenzhen China

Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detection precision and false alarm rates. To break this dilemma, we propose SeRankDet, a deep network that achieves high accuracy beyond the conventional hit-miss trade-off, by following the "Pick of the Bunch" principle. At its core lies our Selective Rank-Aware Attention (SeRank) module, employing a non-linear Top-K selection process that preserves the most salient responses, preventing target signal dilution while maintaining constant complexity. Furthermore, we replace the static concatenation typical in U-Net structures with our Large Selective Feature Fusion (LSFF) module, a dynamic fusion strategy that empowers SeRankDet with adaptive feature integration, enhancing its ability to discriminate true targets from false alarms. The network’s discernment is further refined by our Dilated Difference Convolution (DDC) module, which merges differential convolution aimed at amplifying subtle target characteristics with dilated convolution to expand the receptive field, thereby substantially improving target-background separation. Despite its lightweight architecture, the proposed SeRankDet sets new benchmarks in state-of-the-art performance across multiple public datasets. The code is available at https://***/GrokCV/SeRankDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

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arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education 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

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

A Security and Trust Protection Framework for Open CNC Production Line

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A Security and Trust Protection Framework for Open CNC Produ...

Chinese Automation Congress (CAC)

作者： Zeyang Dong Yuanqing Qin Zewei Li Hanwen Wang School of Artificial Intelligence and Automation MOE Lab of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

With the further integration of information technology and industry, the computer numerical control (CNC) production line has gradually changed from the original isolated and closed mode to an open one. It faces not only internal threats, but also threats from the Internet. Starting from the characteristics and cybersecurity problem analysis of open CNC production lines, this paper proposes a security and trust protection framework based on trusted computing 3.0 and the concept of defense in depth, where a tri-element peer architecture is introduced to realize two-way authentication between the access device and the platform and an active immunity scheme for open CNC system is designed to ensure the trust from the source, and the trusted NC-Link agent/adaper are utilized to connect the heterogeneous equipments trustedly in the production line. The proposed framework can improve the information security level of the open CNC production line while maintaining its openness.

关键词： Trusted computing Processor scheduling Operating systems Authentication Optimization methods Production Computer architecture

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide image Classification 38

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide...

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Fu, Kexue Luo, Xiaoyuan Qu, Linhao Wang, Shuo Xiong, Ying Maglogiannis, Ilias Gao, Longxiang Wang, Manning Qilu University of Technology Shandong Academy of Sciences Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China Fudan University China University of Piraeus Greece Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

Tri-Perspective view Decomposition for Geometry-Aware Depth Completion

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Tri-Perspective view Decomposition for Geometry-Aware Depth ...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiqiang Yan Yuankai Lin Kun Wang Yupeng Zheng Yufei Wang Zhenyu Zhang Jun Li Jian Yang 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 Sci. & Tech. China Huazhong University of Science and Technology Chinese Academy of Sciences Northwestem Poly technical University Nanjing University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Depth completion is a vital taskfor autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. How-ever, most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation, which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this chal-lenge, we introduce Tri-Perspective View Decomposition (TPVD), a novel framework that can explicitly model 3D geometry. In particular, (1) TPVD ingeniously decomposes the original point cloud into three 2D views, one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation, where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPVaffinitive neighbors, the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result, our TPVD outperforms existing methods on KITTI, NYUv2, and SUN RGBD. Fur-thermore, we build a novel depth completion dataset named TOFDC, which is acquired by the time-of-flight (TOF) sen-sor and the color camera on smart phones. Project page.

关键词： Geometry Point cloud compression Solid modeling Three-dimensional displays Laser radar Convolution Pattern recognition

Transformer for Multitemporal Hyperspectral image Unmixing

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arXiv 2024年

作者： Li, Hang Dong, Qiankun Xie, Xueshuo Xu, Xia Li, Tao Shi, Zhenwei The College of Computer Science Nankai University Tianjin300071 China The Haihe Lab of ITAI Street Tianjin300350 China The School of Computer Science and Technology Tiangong University Tianjin300387 China The Image Processing Center School of Astronautics Beihang University Beijing100191 China

Multitemporal hyperspectral image unmixing (MTHU) holds significant importance in monitoring and analyzing the dynamic changes of surface. However, compared to single-temporal unmixing, the multitemporal approach demands comprehensive consideration of information across different phases, rendering it a greater challenge. To address this challenge, we propose the Multitemporal Hyperspectral image Unmixing Transformer (MUFormer), an end-to-end unsupervised deep learning model. To effectively perform multitemporal hyperspectral image unmixing, we introduce two key modules: the Global Awareness Module (GAM) and the Change Enhancement Module (CEM). The Global Awareness Module computes self-attention across all phases, facilitating global weight allocation. On the other hand, the Change Enhancement Module dynamically learns local temporal changes by comparing endmember changes between adjacent phases. The synergy between these modules allows for capturing semantic information regarding endmember and abundance changes, thereby enhancing the effectiveness of multitemporal hyperspectral image unmixing. We conducted experiments on one real dataset and two synthetic datasets, demonstrating that our model significantly enhances the effect of multitemporal hyperspectral image unmixing. © 2024, CC BY.

关键词： Deep learning

Divide and Conquer: Hybrid Pre-training for Person Search

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arXiv 2023年

作者： Tian, Yanling Chen, Di Liu, Yunan Yang, Jian Zhang, Shanshan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education 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 School of Artificial Intelligence Dalian Maritime University China

Large-scale pre-training has proven to be an effective method for improving performance across different tasks. Current person search methods use imageNet pre-trained models for feature extraction, yet it is not an optimal solution due to the gap between the pre-training task and person search task (as a downstream task). Therefore, in this paper, we focus on pretraining for person search, which involves detecting and reidentifying individuals simultaneously. Although labeled data for person search is scarce, datasets for two sub-tasks person detection and re-identification are relatively abundant. To this end, we propose a hybrid pre-training framework specifically designed for person search using sub-task data only. It consists of a hybrid learning paradigm that handles data with different kinds of supervisions, and an intra-task alignment module that alleviates domain discrepancy under limited resources. To the best of our knowledge, this is the first work that investigates how to support full-task pre-training using sub-task data. Extensive experiments demonstrate that our pre-trained model can achieve significant improvements across diverse protocols, such as person search method, finetuning data, pre-training data and model backbone. For example, our model improves ResNet50 based NAE by 10.3% relative improvement w.r.t. mAP. Our code and pre-trained models are released for plug-and-play usage to the person search community (https://***/personsearch/PretrainPS). Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Diff-Reg: Diffusion Model in Doubly Stochastic Matrix Space for Registration Problem

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arXiv 2024年

作者： Wu, Qianliang Jiang, Haobo Luo, Lei Li, Jun Ding, Yaqing Xie, Jin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education 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 State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic National University of Singapore Singapore

Establishing reliable correspondences is essential for 3D and 2D-3D registration tasks. Existing methods commonly leverage geometric or semantic point features to generate potential correspondences. However, these features may face challenges such as large deformation, scale inconsistency, and ambiguous matching problems (e.g., symmetry). Additionally, many previous methods, which rely on single-pass prediction, may struggle with local minima in complex scenarios. To mitigate these challenges, we introduce a diffusion matching model for robust correspondence construction. Our approach treats correspondence estimation as a denoising diffusion process within the doubly stochastic matrix space, which gradually denoises (refines) a doubly stochastic matching matrix to the ground-truth one for high-quality correspondence estimation. It involves a forward diffusion process that gradually introduces Gaussian noise into the ground truth matching matrix and a reverse denoising process that iteratively refines the noisy one. In particular, we deploy a lightweight denoising strategy during the inference phase. Specifically, once points/image features are extracted and fixed, we utilize them to conduct multiple-pass denoising predictions in the reverse sampling process. Evaluation of our method on both 3D and 2D-3D registration tasks confirms its effectiveness. The code is available at https://***/wuqianliang/Diff-Reg. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

Lightweight Human Pose Estimation Using Heatmap-Weighting Loss

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arXiv 2022年

作者： Li, Shiqi Xiang, Xiang MoE Key Lab of Image Information Processing Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that utilizes original, inter-level, and intra-level information to intensify the accuracy. Additionally, we propose a novel loss function called heatmap weighting loss, which generates weights for each pixel on the heatmap that makes the model more focused on keypoints. Experiments demonstrate our method achieves a balance between performance, resource volume, and inference speed. Specifically, our method can achieve 65.3 AP score on COCO test-dev, while the inference speed is 55 FPS and 18 FPS on the mobile GPU and CPU, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking