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Graph Matching Optimization network for Point Cloud Registration

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arXiv 2023年

作者： Wu, Qianliang Shen, Yaqi Jiang, Haobo Mei, Guofeng Ding, Yaqing Luo, Lei Xie, Jin Yang, Jian 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 China The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia

Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize the geometric structure information in point feature embedding or outlier rejection for registration while neglecting to consider explicitly isometry-preserving constraint (e.g., point pair linked edge’s length preserving after transformation) in training. We claim that the explicit isometry-preserving constraint is also important for improving feature representation abilities in the feature training stage. To this end, we propose a Graph Matching Optimization based network (GMONet for short), which utilizes the graph-matching optimizer to explicitly exert the isometry preserving constraints in the point feature training to improve the point feature representation. Specifically, we exploit a partial graph-matching optimizer to optimize the super point (i.e., down-sampled key points) features and a full graph-matching optimizer to optimize fine-level point features in the overlap region. Meanwhile, we leverage the inexact proximal point method and the mini-batch sampling technique to accelerate these two graph-matching optimizers. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The experimental results show that our method performs competitively compared to state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface measurement

A New Construction Structure on Coded Caching with Linear Subpacketization: Non-Half-Sum Disjoint Packing

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arXiv 2025年

作者： Cheng, Minquan Wei, Huimei Wan, Kai Caire, Giuseppe Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China the School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China the Electrical Engineering and Computer Science Department Technische Universität Berlin Berlin10587 Germany

Coded caching is a promising technique to effectively reduce peak traffic by using local caches and the multicast gains generated by these local caches. Coded caching schemes have been widely investigated following the seminal work of Maddah-Ali and Niesen. Explicit coding constructions have been proposed for a variety of network topologies with information-theoretically optimal or near-optimal transmission load R. An important parameter in these constructions is the subpacketization F, i.e., the number of subpackets that each content file needs to be divided into. In particular, the original scheme of Maddah-Ali and Niesen as well as several other variants require F to grow exponentially with the number of users K. In practice, files have finite size and too large F yields impractically small subpackets. Therefore, it is important to design coded caching schemes with F and R as small as possible. At present, the few known schemes with subpacketization linear in K achieve large load. In this paper, we consider the linear scaling regime F = O(K) and design schemes with unprecedented transmission load R. Specifically, we first introduce a new combinatorial structure called non-half-sum disjoint packing (NHSDP) which can be used to generate a coded caching scheme with K = O(F). A class of new schemes is then obtained by constructing NHSDP. Theoretical and numerical analyses demonstrate that (i) in comparison to existing schemes with linear subpacketization, the proposed scheme achieves a lower load;(ii) the proposed scheme also attains a lower load than some existing schemes with polynomial subpacketization in some cases;and (iii) the proposed scheme achieves load values comparable to those of existing schemes with exponential subpacketization in some cases. Furthermore, the newly introduced concept of NHSDP is closely related to classical combinatorial structures, including cyclic difference packings (CDP), non-three-term arithmetic progressions (NTAP), and perfect ha

关键词： Combinatorial mathematics

Not all diffusion model activations have been evaluated as discriminative features 24

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Not all diffusion model activations have been evaluated as d...

Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at https://***/Darkbblue/generic-diffusion-feature.

关键词：

Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective

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arXiv 2024年

作者： Li, De Li, Xianxian Gan, Zeming Li, Qiyu Qu, Bin Wang, Jinyan Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

Graph neural networks (GNNs) based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy labeling approaches focus on the visual domain or graph node classification tasks and analyze the impact of noisy labels only from a utility perspective. Unlike existing work, in this paper, we measure the effects of noise labels on graph classification from data privacy and model utility perspectives. We find that noise labels degrade the model’s generalization performance and enhance the ability of membership inference attacks on graph data privacy. To this end, we propose the robust graph neural network (RGLC) approach with noisy labeled graph classification. Specifically, we first accurately filter the noisy samples by high-confidence samples and the first feature principal component vector of each class. Then, the robust principal component vectors and the model output under data augmentation are utilized to achieve noise label correction guided by dual spatial information. Finally, supervised graph contrastive learning is introduced to enhance the embedding quality of the model and protect the privacy of the training graph data. The utility and privacy of the proposed method are validated by comparing twelve different methods on eight real graph classification datasets. Compared with the state-of-the-art methods, the RGLC method achieves at most and at least 7.8% and 0.8% performance gain at 30% noisy labeling rate, respectively, and reduces the accuracy of privacy attacks to below 60%. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

Adaptive and Background-Aware Match for Class-Agnostic Counting

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SSRN

SSRN 2023年

作者： Gong, Shenjian Yang, Jian Zhang, Shanshan 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 Nanjing210094 China Nankai University China

Class-agnostic counting (CAC) aims to count object instances in an image by simply specifying a few exemplar boxes of interest. The key challenge for CAC is how to tailor a desirable interaction between exemplar and query features. Previous CAC methods implement such interaction by solely leveraging standard global feature convolution. We find this interaction leads to under-match caused by intra-class diversity and over-match on background, which severely degrades counting performance. In this work, we propose a novel feature interaction method called Adaptive and Background-Aware Match (ABM) against high intra-class diversity and noisy background. Concretely, given exemplar and query features, we improve the original high-dimensional coupled space match to adaptive orthogonal subspace match (AOM), avoiding under-match caused by intra-class diversity. Moreover, background-specific match (BSM) employs interaction between learnable background prototype and query feature to provide global background priors, making match be aware of background regions. Additionally, we find the high scale variance among different query images leads to bad counting performance for extremely small scale objects. Object scale unify (OSU) is proposed to take the size of the exemplars as the scale prior and resize query images such that all objects are in a uniform average scale. Extensive experiments on FSC-147 show that our method outperforms state-of-the-art approaches. We also conduct extensive ablation studies to demonstrate the effectiveness of each component of our proposed method. The code is publicly available at https://***/Yankeegsj/ABMNet. © 2023, The Authors. All rights reserved.

关键词： Machine learning

View-decoupled Transformer for Person Re-identification under Aerial-ground Camera network

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View-decoupled Transformer for Person Re-identification unde...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Quan Zhang Lei Wang Vishal M. Patel Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Department of Electrical and Computer Engineering Johns Hopkins University USA Pazhou Lab (HuangPu) Guangdong China Guangdong Province Key Laboratory of Information Security Technology Guangdong China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： computer vision Cameras Transformers Pattern recognition Computational complexity Identification of persons

Semi-Supervised Image Captioning Considering Wasserstein Graph Matching

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arXiv 2024年

作者： Yang, Yang The Nanjing University of Science and Technology Nanjing210094 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 China Ministry of Education State Key Lab. for Novel Software Technology Nanjing University China

Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that describing images always requires a huge of manpower, we usually have limited amount of described images (i.e., image-text pairs) and a large number of undescribed images in real-world applications. Thereby, a dilemma is the "Semi-Supervised Image Captioning". To solve this problem, we propose a novel Semi-Supervised Image Captioning method considering Wasserstein Graph Matching (SSIC-WGM), which turns to adopt the raw image inputs to supervise the generated sentences. Different from traditional single modal semi-supervised methods, the difficulty of semi-supervised cross-modal learning lies in constructing intermediately comparable information among heterogeneous modalities. In this paper, SSIC-WGM adopts the successful scene graphs as intermediate information, and constrains the generated sentences from two aspects: 1) inter-modal consistency. SSIC-WGM constructs the scene graphs of the raw image and generated sentence respectively, then employs the wasserstein distance to better measure the similarity between region embeddings of different graphs. 2) intra-modal consistency. SSIC-WGM takes the data augmentation techniques for the raw images, then constrains the consistency among augmented images and generated sentences. Consequently, SSIC-WGM combines the cross-modal pseudo supervision and structure invariant measure for efficiently using the undescribed images, and learns more reasonable mapping function. Experiments show that our method can outperform state-of-the-art comparison methods on the MS-COCO "Karpathy" offline test split, under different complex semi-supervised scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

FusionTC: Encrypted App Traffic Classification Using Decision-Level Multimodal Fusion Learning of Flow Sequence

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Li, Shengbao Huang, Yuchuan Gao, Tianye Yang, Lanqi Chen, Yige Pan, Quanbo Zang, Tianning Chen, Fei Institute of Information Engineering Chinese Academy of Sciences Beijing100045 China School of Cyber Security University of Chinese Academy of Sciences Beijing101408 China National Computer Network Emergency Response Technical Team/Coordination Center of China Shandong Branch Jinan250004 China Ministry of Emergency Management Big Data Center Beijing100010 China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal University Anhui Wuhu240002 China College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325000 China National Computer Network Emergency Response Technical Team/Coordination Center of China Xinjiang Branch Urumqi830000 China

In recent years, an increasing number of mobile platforms and applications have adopted traffic encryption protocol technology to ensure privacy and security. Existing researches on encrypted traffic identification approaches often rely on a single-modal feature pattern (such as packet sequence and statistical features), which cannot fully represent the detail information of complex traffic features, and so their predictions are susceptible to anomalies. In order to improve the effect of classification on encrypted app traffic, we propose FusionTC, a novel app traffic classification framework based on feature fusion of flow sequence. FusionTC consists of two-level subclassifiers, which are used to perform decision-level fusion of multimodal features by an upgraded stacking method. The comprehensive capture and fusion of multimodal traffic details, coupled with the refined processing and segmentation of traffic, enables FusionTC to significantly promote classification accuracy and enhance robustness in challenging situations. Based on our self-built app traffic dataset, FusionTC improves the accuracy by at least 3.2% over the state-of-the-art approaches. © 2023 Shengbao Li et al.

关键词： Cryptography

HSIGene: A Foundation Model for Hyperspectral Image Generation

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arXiv 2024年

作者： Pang, Li Cao, Xiangyong Tang, Datao Xu, Shuang Bai, Xueru Zhou, Feng Meng, Deyu The School of Computer Science and Technology The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi’an710021 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China Xidian University Xi’an710071 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://***/LiPang/HSIGene. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement