OpenRaiser/Intern-Atlas huggingface dataset:
ProCom: A few-shot targeted community detection algorithm
Authors: Xixi Wu, Kaiyu Xiong, Yun Xiong, Xiaoxin He, Yao Zhang 0009, Yizhu Jiao, Jiawei Zhang 0001
Venue: Conference KDD 2024
Year: 2024

Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying fraud groups in transaction networks. Traditional community detection methods fail to capture the specific features of the targeted community and detect all types of communities indiscriminately. Semi-supervised community detection algorithms, emerged as a feasible alternative, are inherently constrained by their limited adaptability and substantial reliance on a large amount of labeled data, which demands extensive domain knowledge and manual effort. In this paper, we address the aforementioned weaknesses in targeted community detection by focusing on few-shot scenarios. We propose ProCom, a novel framework that extends the ``pre-train, prompt'' paradigm, offering a low-resource, high-efficiency, and transferable solution. Within the framework, we devise a dual-level context-aware pre-training method that fosters a deep understanding of latent communities in the network, establishing a rich knowledge foundation for downstream task. In the prompt learning stage, we reformulate the targeted community detection task into pre-training objectives, allowing the extraction of specific knowledge relevant to the targeted community to facilitate effective and efficient inference. By leveraging both the general community knowledge acquired during pre-training and the specific insights gained from the prompt communities, ProCom exhibits remarkable adaptability across different datasets. We conduct extensive experiments on five benchmarks to evaluate the ProCom framework, demonstrating its SOTA performance under few-shot scenarios, strong efficiency, and transferability across diverse datasets.
A pattern-driven information diffusion prediction model based on multisource resonance and cognitive adaptation
Authors: Weikang He, Yunpeng Xiao, Mengyang Huang, Xuemei Mou, Rong Wang, Qian Li
Venue: Conference SIGIR 2025
Year: 2025

The significant societal impact of online public opinion has spurred extensive research into the underlying mechanisms of information diffusion. While existing diffusion prediction models have effectively leveraged network structure, they often lack a deep understanding of the intrinsic patterns governing information dissemination, thus limiting their predictive power. To address this critical gap, we introduce PMRCA, a novel information diffusion prediction model inspired by social psychology and driven by fundamental propagation patterns. PMRCA posits that two key patterns underpin topic propagation: multisource resonance, where consensus emerges from consistent narratives across multiple sources, and cognitive adaptation, where information is tailored to align with audience cognitive features. Correspondingly, PMRCA incorporates two dedicated learning tasks: structural contrastive learning based on node homogeneity to capture multisource resonance, and clustering contrastive learning based on cognitive clusters to model cognitive adaptation. Extensive experiments on four real-world datasets demonstrate that PMRCA significantly outperforms existing mainstream models. Importantly, our pattern-driven approach is model-agnostic, allowing it to be flexibly applied to various diffusion prediction scenarios and enhance performance.
Foundation models for spatio-temporal data science: A tutorial and survey
Authors: Yuxuan Liang, Haomin Wen, Yutong Xia, Ming Jin, Bin Yang, Flora Salim, Qingsong Wen, Shirui Pan, Gao Cong
Venue: Conference KDD 2025
Year: 2025

Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation. Traditional deep learning approaches have significantly advanced this field, particularly in the stage of ST data mining. However, these models remain task-specific and often require extensive labeled data. Inspired by the success of Foundation Models (FM), especially large language models, researchers have begun exploring the concept of Spatio-Temporal Foundation Models (STFMs) to enhance adaptability and generalization across diverse ST tasks. Unlike prior architectures, STFMs empower the entire workflow of ST data science, ranging from data sensing, management, to mining, thereby offering a more holistic and scalable approach. Despite rapid progress, a systematic study of STFMs for ST data science remains lacking. This survey aims to provide a comprehensive review of STFMs, categorizing existing methodologies and identifying key research directions to advance ST general intelligence.
G2PTL: A geography-graph pre-trained model
Authors: Lixia Wu, Jianlin Liu, Junhong Lou, Minhui Deng, Jianbin Zheng, Haomin Wen, Chao Song, Shu He
Venue: Conference CIKM 2024
Year: 2024

As an important data resource containing spatial information, addresses record the geospatial information corresponding to social production activities and human behavioral activities. How to effectively encode addresses has always been a core challenge in the field of Geographic Information Systems (GIS). Pre-trained Models (PTMs) designed for Natural Language Process (NLP) have emerged as the dominant tools for encoding semantic information in text. Though promising, those NLP-based PTMs fall short of encoding geographic knowledge in addresses, which limits their application potential in geospatial tasks. To tackle the above problem, this study proposes a Geography-Graph Pre-trained model (G2PTL) that combines graph learning and text pre-training, aiming to make up for the shortcomings of traditional PTM in the geography field. Specifically, we first utilize real-world delivery data to build a large-scale heterogeneous graph of addresses, which contains abundant geographic knowledge and spatial topology information. Then, G2PTL is pre-trained with subgraphs sampled from the heterogeneous graph. Through experimental evaluation on multiple downstream tasks of GIS, including geocoding, geographic entity prediction, and geographic entity recognition, G2PTL demonstrated significant performance improvements. G2PTL has been successfully deployed in production-level GIS, such as Cainiao's logistics system, effectively improving the execution efficiency and accuracy of address-related tasks. This research not only provides a new technical path for the encoding and processing of geographical information, but also opens up a new perspective for the study of pre-training models in the geographical field. The code resources of the G2PTL model have been opened for research and application developers to access and use at https://huggingface.co/Cainiao-AI/G2PTL.
Diffcrime: A multimodal conditional diffusion model for crime risk map inference
Authors: Shuliang Wang 0001, Xinyu Pan, Sijie Ruan, Haoyu Han 0003, Ziyu Wang, Hanning Yuan, Jiabao Zhu, Qi Li 0022
Venue: Conference KDD 2024
Year: 2024

Crime risk map plays a crucial role in urban planning and public security management. Traditionally, it is obtained solely from historical crime incidents or inferred from limited environmental factors, which are not sufficient to accurately model the occurrences of crimes over the geographical space well. Motivated by the impressive and realistic conditional generating power of diffusion models, in this paper, we propose a multimodal conditional diffusion method, namely, DiffCrime, to infer the crime risk map based on datasets in various domains, i.e., historical crime incidents, satellite imagery, and map imagery. It is equipped with a history-gated multimodal denoising network, i.e., HamNet, dedicated to the crime risk map inference. HamNet emphasizes the importance of historical crime data via a Gated-based History Fusion (GHF) module and adaptively controls multimodal conditions to be fused across different diffusion time steps via a Time step-Aware Modality Fusion (TAMF) module. Extensive experiments on two real-world datasets demonstrate the effectiveness of DiffCrime, which outperforms baselines by at least 43% and 31% in terms of RMSE, respectively.
MSTEM: Masked spatiotemporal event series modeling for urban undisciplined events forecasting
Authors: Zehao Gu, Shiyang Zhou, Yun Xiong, Yang Luo, Hongrun Ren, Qiang Wang, Xiaofeng Gao, Philip S. Yu
Venue: Conference CIKM 2024
Year: 2024

Urban undisciplined events (UUE) are of increasing concern to urban officials because they reduce the quality of life and cause societal disorder. How to accurately predict future occurrences is a key point in preventing these events. However, existing supervised methods struggle to perform well on sparse UUEs while self-supervised MAE-based methods adopt a traditional random masking strategy which leads to limited performance on UUE forecasting. Fortunately, we have designed an innovative spatiotemporal masking strategy and its corresponding pre-training task called Masked Spatio-Temporal Event Series Modeling (MSTEM). Through Cluster-assisted region masking, MSTEM efficiently distributes masked regions evenly among different clusters, enhancing the model's ability to capture spatial correlation and heterogeneity while addressing sparse region distribution of UUEs. Frequency-enhanced patch masking helps the model to sufficiently extract the temporal features of UUEs by reconstructing multiple views. Additionally, we propose future merge and cluster label modeling to enhance the extraction of spatiotemporal dependencies, thereby improving the performance of MSTEM on downstream prediction tasks. Experimental evaluations on four real-world datasets including crimes and disorderly conduct show that our masked autoencoder with MSTEM outperforms most of the state-of-the-art baselines.
Protomix: Augmenting health status representation learning via prototype-based mixup
Authors: Yongxin Xu, Xinke Jiang, Xu Chu, Yuzhen Xiao, Chaohe Zhang, Hongxin Ding, Junfeng Zhao 0001, Yasha Wang, Bing Xie
Venue: Conference KDD 2024
Year: 2024

With the widespread adoption of electronic health records (EHR) data, deep learning techniques have been broadly utilized for various health prediction tasks. Nevertheless, the labeled data scarcity issue restricts the prediction power of these deep models. To enhance the generalization capability of deep learning models when faced with such situations, a common trend is to train generative adversarial networks (GANs) or diffusion models for data augmentation. However, due to limitations in sample size and potential label imbalance issues, these methods are prone to mode collapse problems. This results in the generation of new samples that fail to preserve the subtype structure within EHR data, thereby limiting their practicality in health prediction tasks that generally require detailed patient phenotyping. Aiming at the above problems, we propose a <u>Proto</u>type-based <u>Mix</u>up method, dubbed ProtoMix, which combines prior knowledge of intrinsic data features from subtype centroids (i.e., prototypes) to guide the synthesis of new samples. Specifically, ProtoMix employs a prototype-guided mixup training task to shift the decision boundary away from the subtypes. Then, ProtoMix optimizes the sampling weights in different areas of the data manifold via a prototype-guided mixup sampling strategy. Throughout the training process, ProtoMix dynamically expands the training distribution using an adaptive mixing coefficient computation method. Experimental evaluations on three real-world datasets demonstrate the efficacy of ProtoMix.
Recognizing 3D Objects Using Photometric Invariant
Authors: Kenji Nagao
Venue: Conference ICCV 1995
Year: 1995

We describe an efficient algorithm for recognizing 3D objects by combining photometric, and geometric invariants. A photometric property is derived, that is invariant to the changes of illumination and to relative object motion with respect to the camera and/or the lighting source in 3D space. We argue that conventional color constancy algorithms can not be used in the recognition of 3D objects. Further we show recognition does not require a full constancy of colors, rather, it only needs something that remains unchanged under the varying light conditions and poses of the objects. Combining the derived color invariant and the spatial constraints on the object surfaces, we identify corresponding positions in the model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. Tests are given to show the stability and efficiency of our approach to 3D object recognition.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
In Defence of the 8-Point Algorithm
Authors: Richard I. Hartley
Venue: Conference ICCV 1995
Year: 1995

The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the 8 point algorithm is a frequently cited method for computing the fundamental matrix from a set of 8 or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. The paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A Binocular Stereo Algorithm for Reconstructing Sloping, Creased, and Broken Surfaces in the Presence of Half- Occlusion
Authors: Peter N. Belhumeur
Venue: Conference ICCV 1993
Year: 1993

The author presents a method for reconstructing the three-dimensional scene geometry, i.e., depth, surface orientation, occluding contours, and surface creases, from a pair of stereo images. This reconstruction is not done as a postprocessing step, but rather all of the quantities are estimated simultaneously as part of the matching algorithm. An energy functional is considered in which each of the quantities in the scene geometry is explicitly represented. For this energy functional, a smoothness prior that, in addition to its ability to detect surface discontinuities and the accompanying half-occluded regions, is able to reconstruct steeply sloping surfaces with sharp creases is used. Experimental results demonstrating the effectiveness of the algorithm are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Robust Histogram Construction from Color Invariants
Authors: Theo Gevers
Venue: Conference ICCV 2001
Year: 2001

A simple and effective object recognition scheme is to represent and march images on the basis of color histograms. To obtain robustness against varying imaging circumstances (e.g. a change in illumination, object pose, and viewpoint), color histograms are constructed from color invariants. However in general, color invariants are negatively affected by sensor noise due to the instabilities of these color invariant transforms at many RGB values. To suppress the effect of noise blow-up for unstable color invariant values, in this paper color invariant histograms are computed using variable kernel density estimators. To apply variable kernel density estimation in a principled way, models are proposed for the propagation of sensor noise through color invariants. As a result the associated uncertainty is known for each color invariant value. The associated uncertainty is used to derive the parameterization of the variable kernel density estimator during histogram construction. It is empirically verified that the proposed method compares favorably to traditional color histograms for object recognition.
Maximum-Likelihood Template Matching
Authors: Clark F. Olson
Venue: Conference CVPR 2000
Year: 2000

In image matching applications such as tracking and stereo matching, it is common to use the sum-of-squared-differences (SSD) measure to determin the best match for an image template. However, this measure is sensitive to outliers and is not robust to template variations. We describe a robust measure and efficient search strategy for template matching with a binary or greyscale temolate using a maximum-likelihood formulation. In addition to subpixel localization and uncertainty estimation, these techniques allow optimal feature selection based on minimizing the localization uncertainty. We examine the use of these techniques for object recognition, stereo matching, feature selection and tracking.
Fast and Reliable Color Region Merging Inspired by Decision Tree Pruning
Authors: Richard Nock
Venue: Conference CVPR 2001
Year: 2001

In this paper, we exploit some previous theoretical results about decision tree pruning to derive a color segmentation algorithm which avoids some of the common drawbacks of region merging techniques. The algorithm has both statistical and computational advantages over known approaches. It authorizes the processing of 512/spl times/512 images in less than a second on conventional PC computers. Experiments are reported on thirty-five images of various origins, illustrating the quality of the segmentations obtained.
Recognizing Algebraic Surfaces from Their Outlines
Authors: David A. Forsyth
Venue: Conference ICCV 1993
Year: 1993

The author shows that the projective invariants of an algebraic surface can be computed from the outline of that surface in a perspective view using an uncalibrated camera, by showing that an outline completely determines the projective geometry of an algebraic surface. A single perspective view of a generic algebraic surface of degree three or greater uniquely determines the projective geometry of the surface. The result holds for an unknown focal point, and an uncalibrated camera. The projective ambiguity is not improved by using a calibrated camera.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Locating Objects Using the Hausdorff Distance
Authors: William Rucklidge
Venue: Conference ICCV 1995
Year: 1995

The Hausdorff distance is a measure defined between two point sets representing a model and an image. In the past, it has been used to search images for instances of a model that has been translated or translated and scaled by finding transformations that bring a large number of model features close to image features, and vice versa. The Hausdorff distance is reliable even when the image contains multiple objects, noise, spurious features, and occlusions. We apply it to the task of locating an affine transformation of a model in an image; this corresponds to determining the pose of a planar object that has undergone weak perspective projection. We develop a rasterised approach to the search and a number of techniques that allow us to quickly locate all transformations of the model that satisfy two quality criteria; we can also quickly locate only the best transformation. We discuss an implementation of this approach, and present some examples of its use.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Grouping Based on Projective Geometry Constraints and Uncertainty
Authors: Sven Utcke
Venue: Conference ICCV 1998
Year: 1998

The process of grouping and subsequently recognising objects in cluttered images is one laden with difficulties; however, results can be greatly enhanced if the inherent uncertainty of image-features is taken into account. This paper shows that starting with the individual edgel's uncertainty it is possible to calculate covariance-information for all derived quantities. This information can be used to choose between competing algorithms, selecting the one that produces the more reliable results, but also as an aid during the recognition process. The consequent application of error-propagation leads to a new formulation for the calculation of the cross-ratio, which is both robust and efficient in dealing with measured lines, and does not require knowledge about the vanishing point. Extensive Monte-Carlo simulations as well as the application to images of cluttered street-scenes demonstrate the robustness and suitability of the approach.
Shape from Rotation
Authors: Richard Szeliski
Venue: Conference CVPR 1991
Year: 1991

The construction of a 3D surface model of an object rotating in front of a camera is examined. Previous research in depth from motion has demonstrated the power of using an incremental approach to depth estimation. The author extends this approach to more general motion and uses a full 3D surface model instead of a 2 1/2 D depth map. The algorithm starts with a flow field computed using local correlation. It then projects individual measurements into 3D points with associated uncertainties. Nearby points from successive frames are merged to improve the position estimates. These points are then used to construct a deformable surface model, which is refined over time. The application of novel techniques to several image sequences is demonstrated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Labeling of Curvilinear Structure Across Scales By Token Grouping
Authors: Eric Saund
Venue: Conference CVPR 1992
Year: 1992

An algorithm for labeling curvilinear structure at multiple scales in line drawings and edge images is presented. Symbolic curve-element tokens residing in a spatially indexed and scale-indexed data structure denote circular arcs fit to image data. Tokens are computed via a small-to-large scale grouping procedure using a greedy best-first strategy for choosing the support of new tokens. The resulting image description is rich and redundant in that a given segment of image contour may be described by multiple tokens at different scales, and by more than one token at any given scale. This property facilitates selection and characterization of portions of the image based on curve-element attributes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Saliency maps and attention selection in scale and spatial coordinates: An information theoretic approach
Authors: Martin Jägersand
Venue: Conference ICCV 1995
Year: 1995

Information measures with respect to spatial locations and scales of objects in an image are important to image processing and interpretation. It allows us to focus attention on relevant data, saving effort and reducing false positives. In particular, the information content of a man-made scene is typically confined to a small set of scales. We devise a scale space based measure of image information. Kullback contrasts between successive resolution lengths gives the differential information gain. Experiments show that this measure gives a clear indication of characteristic lengths in a variety of real world images and is superior to power spectrum based measurements. Decomposing the expected information gain into spatial coordinates gives us a saliency map for use by an attention selector. We combine the scale and spatial decompositions into a single information measure, giving both the spatial extent and scale range of interest. The information measure has an efficient implementation, and thus can be used routinely in early vision processing.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>