OpenRaiser/Intern-Atlas huggingface dataset:
Effective Clustering on Large Attributed Bipartite Graphs
Authors: Renchi Yang, Yidu Wu, Xiaoyang Lin, Qichen Wang 0001, Tsz Nam Chan, Jieming Shi 0001
Venue: Conference KDD 2024
Year: 2024

Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship graphs. Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds widespread use in various domains, including social network analysis, recommendation systems, information retrieval, and bioinformatics. However, the majority of existing solutions towards k-ABGC either overlook attribute information or fail to capture bipartite graph structures accurately, engendering severely compromised result quality. The severity of these issues is accentuated in real ABGs, which often encompass millions of nodes and a sheer volume of attribute data, rendering effective k-ABGC over such graphs highly challenging. In this paper, we propose TPO, an effective and efficient approach to k-ABGC that achieves superb clustering performance on multiple real datasets. TPO obtains high clustering quality through two major contributions: (i) a novel formulation and transformation of the k-ABGC problem based on multi-scale attribute affinity specialized for capturing attribute affinities between nodes with the consideration of their multi-hop connections in ABGs, and (ii) a highly efficient solver that includes a suite of carefully-crafted optimizations for sidestepping explicit affinity matrix construction and facilitating faster convergence. Extensive experiments, comparing TPO against 19 baselines over 5 real ABGs, showcase the superior clustering quality of TPO measured against ground-truth labels. Moreover, compared to the state of the arts, TPO is often more than 40x faster over both small and large ABGs.
Spatial clustering regression of count value data via bayesian mixture of finite mixtures
Authors: Peng Zhao 0021, Hou-Cheng Yang, Dipak K. Dey, Guanyu Hu 0002
Venue: Conference KDD 2023
Year: 2023

Investigating relationships between response variables and covariates in areas such as environmental science, geoscience, and public health is an important endeavor. Based on a Bayesian mixture of finite mixtures model, we present a novel spatially clustered coefficients regression model for count value data. The proposed method detects the spatial homogeneity of the Poisson regression coefficients. A Markov random field constrained mixture of finite mixtures prior provides a regularized estimator of the number of clusters of regression coefficients with geographical neighborhood information. As a by-product, we also provide the theoretical properties of our proposed method when the Markov random field is exchangeable. An efficient Markov chain Monte Carlo algorithm is developed by using the multivariate log gamma distribution as a base distribution. Simulation studies are carried out to examine the empirical performance of the proposed method. Additionally, we analyze Georgia's premature death data as an illustration of the effectiveness of our approach. The supplementary materials are provided on GitHub at https://github.com/pengzhaostat/MLG_MFM.
Segmentation by nonlinear diffusion, II
Authors: Jayant Shah
Venue: Conference CVPR 1992
Year: 1992

An algorithm that systematically uses nonuniform smoothing to find boundary components in the form of connected, regularized curves is presented. The boundary, represented by a variable continuously defined over the image domain, as well as the smoothing of the image are determined by a nonlinear system of diffusion equations. Nonlinear diffusion is used again to threshold the boundary variable to produce the actual object boundaries. Laplacians of smoothed gradients are the main tool used. Nonuniform smoothing permits the use of multiple smoothings and the use of derivatives of up to order six.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Stochastic processes in vision: From langevin to Beltrami
Authors: Nir A. Sochen
Venue: Conference ICCV 2001
Year: 2001

Diffusion processes which are widely used in low level vision are presented as a result of an underlying stochastic process. The short-time non-linear diffusion is interpreted as a Fokker-Planck equation which governs the evolution in time of a probability distribution for a Brownian motion on a Riemannian surface. The non linearity of the diffusion has a direct relation to the geometry of the surface. A short time kernel to the diffusion as well as generalizations are found.
Fast and high-performance template matching method
Authors: Alexander Sibiryakov
Venue: Conference CVPR 2011
Year: 2011

This paper proposes a new template matching method that is robust to outliers and fast enough for real-time operation. The template and image are densely transformed in binary code form by projecting and quantizing histograms of oriented gradients. The binary codes are matched by a generic method of robust similarity applicable to additive match measures, such as L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> - and Hamming distances. The robust similarity map is computed efficiently via a proposed Inverted Location Index structure that stores pixel locations indexed by their values. The method is experimentally justified in large image patch datasets. Challenging applications, such as intra-category object detection, object tracking, and multimodal image matching are demonstrated.
Video summarization using singular value decomposition
Authors: Yihong Gong, Xin Liu 0046
Venue: Conference CVPR 2000
Year: 2000

The authors propose a novel technique for video summarization based on singular value decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able, to not only derive the refined feature space to better cluster visually similar frames, but also define a metric to measure the amount of visual content contained in each frame cluster using its degree of visual changes. Then, in the refined feature space, we find the most static frame cluster, define it as the content unit, and use the context value computed from it as the threshold to cluster the rest of the frames. Based on this clustering result, either the optimal set of keyframes, or a summarized motion video with the user specified time length can be generated to support different user requirements for video browsing and content overview. Our approach ensures that the summarized video representation contains little redundancy, and gives equal attention to the same amount of contents.
Action recognition in videos acquired by a moving camera using motion decomposition of lagrangian particle trajectories
Authors: Shandong Wu, Omar Oreifej, Mubarak Shah
Venue: Conference ICCV 2011
Year: 2011

Recognition of human actions in a video acquired by a moving camera typically requires standard preprocessing steps such as motion compensation, moving object detection and object tracking. The errors from the motion compensation step propagate to the object detection stage, resulting in miss-detections, which further complicates the tracking stage, resulting in cluttered and incorrect tracks. Therefore, action recognition from a moving camera is considered very challenging. In this paper, we propose a novel approach which does not follow the standard steps, and accordingly avoids the aforementioned difficulties. Our approach is based on Lagrangian particle trajectories which are a set of dense trajectories obtained by advecting optical flow over time, thus capturing the ensemble motions of a scene. This is done in frames of unaligned video, and no object detection is required. In order to handle the moving camera, we propose a novel approach based on low rank optimization, where we decompose the trajectories into their camera-induced and object-induced components. Having obtained the relevant object motion trajectories, we compute a compact set of chaotic invariant features which captures the characteristics of the trajectories. Consequently, a SVM is employed to learn and recognize the human actions using the computed motion features. We performed intensive experiments on multiple benchmark datasets and two new aerial datasets called ARG and APHill, and obtained promising results.
Probabilistic text modeling with orthogonalized topics
Authors: Enpeng Yao, Guoqing Zheng, Ou Jin, Shenghua Bao, Kailong Chen, Zhong Su, Yong Yu
Venue: Conference SIGIR 2014
Year: 2014

Topic models have been widely used for text analysis. Previous topic models have enjoyed great success in mining the latent topic structure of text documents. With many efforts made on endowing the resulting document-topic distributions with different motivations, however, none of these models have paid any attention on the resulting topic-word distributions.Since topic-word distribution also plays an important role in the modeling performance,topic models which emphasize only the resulting document-topic representations but pay less attention to the topic-term distributions are limited. In this paper, we propose the Orthogonalized Topic Model(OTM) which imposes an orthogonality constraint on the topic-term distributions. We also propose a novel model fitting algorithm based on the generalized Expectation-Maximization algorithm and the Newthon-Raphson method. Quantitative evaluation of text classification demonstrates that OTM outperforms other baseline models and indicates the important role played by topic orthogonalizing.
A statistical approach to background subtraction for surveillance systems
Authors: Naoya Ohta
Venue: Conference ICCV 2001
Year: 2001

Background subtraction is a commonly used process in surveillance systems. One difficult problem when using the process is maintaining a correct background image against changing illumination conditions. Most methods for maintaining the background image are based on intuitive definitions about the illumination change and are implemented as somewhat ad hoc algorithms. In contrast, we first define mathematical models representing the relation between the illumination intensity, a reflection index of objects and a pixel value. We also mathematically define an assumption about illumination, which requires that the distribution of the illumination intensity in a small region does not change. Then we formalize the background subtraction problem as a statistical test (/spl chi//sup 2/ test) based on the models and assumption. The experiments show that our models appropriately express the imaging process of a camera and our method provides stable detection performance for foreground objects.
Dual learning for cross-domain image captioning
Authors: Wei Zhao, Wei Xu, Min Yang, Jianbo Ye, Zhou Zhao, Yabing Feng, Yu Qiao
Venue: Conference CIKM 2017
Year: 2017

Recent AI research has witnessed increasing interests in automatically generating image descriptions in text, which is coined as theimage captioning problem. Significant progresses have been made in domains where plenty of labeled training data (i.e. image-text pairs) are readily available or collected. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying image captioning methods to a new domain. In this paper, we propose a cross-domain image captioning approach that uses a novel dual learning mechanism to overcome this barrier. First, we model the alignment between the neural representations of images and that of natural languages in the source domain where one can access sufficient labeled data. Second, we adjust the pre-trained model based on examining limited data (or unpaired data) in the target domain. In particular, we introduce a dual learning mechanism with a policy gradient method that generates highly rewarded captions. The mechanism simultaneously optimizes two coupled objectives: generating image descriptions in text and generating plausible images from text descriptions, with the hope that by explicitly exploiting their coupled relation, one can safeguard the performance of image captioning in the target domain. To verify the effectiveness of our model, we use MSCOCO dataset as the source domain and two other datasets (Oxford-102 and Flickr30k) as the target domains. The experimental results show that our model consistently outperforms previous methods for cross-domain image captioning.
IVCR-200K: A large-scale multi-turn dialogue benchmark for interactive video corpus retrieval
Authors: Ning Han, Yawen Zeng, Shaohua Long, Chengqing Li, Sijie Yang, Dun Tan, Jianfeng Dong, Jingjing Chen
Venue: Conference SIGIR 2025
Year: 2025

In recent years, significant developments have been made in both video retrieval and video moment retrieval tasks, which respectively retrieve complete videos or moments for a given text query. These advancements have greatly improved user satisfaction during the search process. However, previous work has failed to establish meaningful ''interaction'' between the retrieval system and the user, and its one-way retrieval paradigm can no longer fully meet the personalization and dynamic needs of at least 80.8% of users. In this paper, we introduce the Interactive Video Corpus Retrieval (IVCR) task, a more realistic setting that enables multi-turn, conversational, and realistic interactions between the user and the retrieval system. To facilitate research on this challenging task, we introduce IVCR-200K, a high-quality, bilingual, multi-turn, conversational, and abstract semantic dataset that supports video retrieval and even moment retrieval. Furthermore, we propose a comprehensive framework based on multi-modal large language models (MLLMs) to help users interact in several modes with more explainable solutions. The extensive experiments demonstrate the effectiveness of our dataset and framework. The datasets, codes, and leaderboards are available at: https://ivcr200k.github.io/IVCR.
A Classification Algorithm for Supporting Object-Oriented Views
Authors: Elke A. Rundensteiner
Venue: Conference CIKM 1994
Year: 1994

In recent years, object-oriented (OO) views have been recognized as a powerful mechanism for customizing the structural as well as behavioral aspects of interfaces to object-oriented databases (OODBs) for diverse users. In this context, classification is concerned with the integration of virtual classes derived using an OO query into one unifying schema. Existing approaches either require the user to explicitly specify the relationship between a virtual class and existing base classes, or they relate a virtual class directly with its source class(es) or with the root of the schema. In this paper, we propose a solution to this classification problem that accomplishes the following goals: (1) generate maximally informative, and thus comprehensible, schemas that explicitly model the subclass relationships between base and virtual classes, and (2) support efficient type resolution for shared property functions by supporting upwards inheritance for both base and virtual classes. Correctness and complexity of the classification algorithm are also discussed.
Evaluating Database Selection Techniques: A Testbed and Experiment
Authors: James C. French, Allison L. Powell, Charles L. Viles, Travis Emmitt, Kevin J. Prey
Venue: Conference SIGIR 1998
Year: 1998

We describe a testbed for database selection techniques and an experiment conducted using this testbed. The testbed is a decomposition of the TREC/TIPSTER data that allows analysis of the data along multiple dimensions, including collection-based and temporal-based analysis. We characterize the subcollections in this testbed in terms of number of documents, queries against which the document,s have been evaluated for relevance, and distribution of relevant documents.
Comparing the Performance of Database Selection Algorithms
Authors: James C. French, Allison L. Powell, James P. Callan, Charles L. Viles, Travis Emmitt, Kevin J. Prey, Yun Mou
Venue: Conference SIGIR 1999
Year: 1999

We compare the performance of two database selection algorithms reported in the literature. Their performance is compared using a common testbed designed specifically for database selection techniques. The testbed is a decomposition of the TREC/TIPSTER data into 236 subcollections. We present results of a recent investigation of the performance of the CORI algorithm and compare the performance with earlier work that examined the performance of gGlOSS. The databases from our testbed were ranked using both the gGlOSS and CORI techniques and compared to the RBR baseline, a baseline derived from TREC relevance judgements. We examined the degree to which CORI and gGlOSS approximate this baseline. Our results confirm our earlier observation that the gGlOSS Ideal(l) ranks do not estimate relevance-based ranks well. We also find that CORI is a uniformly better estimator of relevance-based ranks than gGlOSS for the test environment used in this study. Part of the advantage of the CORI algorithm can be explained by a strong correlation between gGlOSS and a size-based baseline (SBR). We also find that CORI produces consistently accurate rankings on testbeds ranging from 100--921 sites. However for a given level of recall, search effort appears to scale linearly with the number of databases.
Noise Reduction in a Statistical Approach to Text Categorization
Authors: Yiming Yang
Venue: Conference SIGIR 1995
Year: 1995

This paper studies noise reduction for computational efficiency improvements in a statistical learning method for text categorization, the Linear Least Squares Fit (LLSF) mapping. Multiple noise reduction strategies are proposedand evaluated, including: an aggressive removal of &quot;non-informative words&quot; from texts before training; the use of a truncated singular value decomposition to cut off noisy &quot;latent semantic structures&quot; during training; the elimination of non-influential components in the LLSF solution (a word-concept association matrix) after training. Text collections in different domains were used for evaluation. Significant improvements in computational efficiency without losing categorization accuracy were evident in the testing results. 1 Introduction The task of text categorization is to assign predefined categories to texts. It has wide application since a controlled vocabulary (subject categories) is often used to index real-world databases for retrieval purposes. While ...
Cooperative Caching by Mobile Clients in Push-Based Information Systems
Authors: Takahiro Hara
Venue: Conference CIKM 2002
Year: 2002

Recent advances in computer and wireless communication technologies have increased interest in push-based information systems in which a server repeatedly broadcasts data to clients through a broadband channel. In this paper, assuming an environment where clients in push-based information systems construct ad hoc networks, we propose three caching strategies in which clients cooperatively cache broadcast data items. These strategies shorten the average response time for data access by replacing cached items based on their access frequencies, the network topology, and the time remaining until each item is broadcast next. We also show the results of simulation experiments conducted to evaluate the performance of our proposed strategies.
Frequent Regular Itemset Mining
Authors: Salvatore Ruggieri
Venue: Conference KDD 2010
Year: 2010

Concise representations of frequent itemsets sacrifice readability and direct interpretability by a data analyst of the concise patterns extracted. In this paper, we introduce an extension of itemsets, called regular, with an immediate semantics and interpretability, and a conciseness comparable to closed itemsets. Regular itemsets allow for specifying that an item may or may not be present; that any subset of an itemset may be present; and that any non-empty subset of an itemset may be present. We devise a procedure, called RegularMine, for mining a set of regular itemsets that is a concise representation of frequent itemsets. The procedure computes a covering, in terms of regular itemsets, of the frequent itemsets in the class of equivalence of a closed one. We report experimental results on several standard dense and sparse datasets that validate the proposed approach.
Topic prediction based on comparative retrieval rankings
Authors: Chris Buckley
Venue: Conference SIGIR 2004
Year: 2004

A new measure, AnchorMap, is introduced to evaluate how close two document retrieval rankings are to each other. It is shown that AnchorMap scores, when run on a set of initial ranked document lists from 8 different systems, are very highly correlated with categorization of topics as easy or hard, and separately, are highly correlated with those topics on which blind feedback works. In another experiment, AnchorMap is used to compare the initial ranked document list from a single system against the ranked document list from that system after blind feedback. Again, high AnchorMap values are highly correlated with both topic difficulty and successful application of blind feedback. Both experiments are examples of using properties of a topic which are independent of relevance information to predict the actual performance of IR systems on the topic. Initial experiments to attempt to improve retrieval performance based upon AnchorMap failed; the causes for failure are discussed.
On rank correlation and the distance between rankings
Authors: Ben Carterette
Venue: Conference SIGIR 2009
Year: 2009

Rank correlation statistics are useful for determining whether a there is a correspondence between two measurements, particularly when the measures themselves are of less interest than their relative ordering. Kendall's - in particular has found use in Information Retrieval as a "meta-evaluation" measure: it has been used to compare evaluation measures, evaluate system rankings, and evaluate predicted performance. In the meta-evaluation domain, however, correlations between systems confound relationships between measurements, practically guaranteeing a positive and significant estimate of - regardless of any actual correlation between the measurements. We introduce an alternative measure of distance between rankings that corrects this by explicitly accounting for correlations between systems over a sample of topics, and moreover has a probabilistic interpretation for use in a test of statistical significance. We validate our measure with theory, simulated data, and experiment.
NRPA: Neural recommendation with personalized attention
Authors: Hongtao Liu, Fangzhao Wu, Wenjun Wang, Xianchen Wang, Pengfei Jiao, Chuhan Wu, Xing Xie
Venue: Conference SIGIR 2019
Year: 2019

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or the similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.