OpenRaiser/Intern-Atlas huggingface dataset:
Multi-image matching using multi-scale oriented patches
Authors: Matthew A. Brown, Richard Szeliski, Simon A. J. Winder
Venue: Conference CVPR 2005
Year: 2005

This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 /spl times/ 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.
Auto-context and its application to high-level vision tasks
Authors: Zhuowen Tu
Venue: Conference CVPR 2008
Year: 2008

The notion of using context information for solving high-level vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with the image appearance, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design, in which the modeling and computing stages are studied in isolation. In this paper, we propose an auto-context algorithm. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates to approach the ground truth. Auto-context learns an integrated low-level and context model, and is very general and easy to implement. Under nearly the identical parameter setting in the training, we apply the algorithm on three challenging vision applications: object segmentation, human body configuration, and scene region labeling. It typically takes about 30 ∼ 70 seconds to run the algorithm in testing. Moreover, the scope of the proposed algorithm goes beyond high-level vision. It has the potential to be used for a wide variety of problems of multi-variate labeling.
A direct least-squares (DLS) method for PnP
Authors: Joel A. Hesch, Stergios I. Roumeliotis
Venue: Conference ICCV 2011
Year: 2011

In this work, we present a Direct Least-Squares (DLS) method for computing all solutions of the perspective-n-point camera pose determination (PnP) problem in the general case (n ≥ 3). Specifically, based on the camera measurement equations, we formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Subsequently, we employ the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters. A key advantage of our method is scalability, since the order of the polynomial system that we solve is independent of the number of points. We compare the performance of our algorithm with the leading PnP approaches, both in simulation and experimentally, and demonstrate that DLS consistently achieves accuracy close to the Maximum-Likelihood Estimator (MLE).
Towards linking buyers and sellers: Detecting commercial intent on twitter
Authors: Bernd Hollerit, Mark Kröll, Markus Strohmaier
Venue: Conference WWW 2013
Year: 2013

Since more and more people use the micro-blogging platform Twitter to convey their needs and desires, it has become a particularly interesting medium for the task of identifying commercial activities. Potential buyers and sellers can be contacted directly thereby opening up novel perspectives and economic possibilities. By detecting commercial intent in tweets, this work is considered a first step to bring together buyers and sellers. In this work, we present an automatic method for detecting commercial intent in tweets where we achieve reasonable precision 57% and recall 77% scores. In addition, we provide insights into the nature and characteristics of tweets exhibiting commercial intent thereby contributing to our understanding of how people express commercial activities on Twitter.
Opportunity model for e-commerce recommendation: right product; right time
Authors: Jian Wang, Yi Zhang
Venue: Conference SIGIR 2013
Year: 2013

Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time.
Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights
Authors: Joel Brooks, Matthew Kerr, John V. Guttag
Venue: Conference KDD 2016
Year: 2016

Quantitative evaluation of the ability of soccer players to contribute to team offensive performance is typically based on goals scored, assists made, and shots taken. In this paper, we describe a novel player ranking system based entirely on the value of passes completed. This value is derived based on the relationship of pass locations in a possession and shot opportunities generated. This relationship is learned by applying a supervised machine learning model to pass locations in event data from the 2012-2013 La Liga season. Interestingly, though this metric is based entirely on passes, the derived player rankings are largely consistent with general perceptions of offensive ability, e.g., Messi and Ronaldo are near the top. Additionally, when used to rank midfielders, it separates the more offensively-minded players from others.
Exploring limits to prediction in complex social systems
Authors: Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts
Venue: Conference WWW 2016
Year: 2016

How predictable is success in complex social systems? In spite of a recent profusion of prediction studies that exploit online social and information network data, this question remains unanswered, in part because it has not been adequately specified. In this paper we attempt to clarify the question by presenting a simple stylized model of success that attributes prediction error to one of two generic sources: insufficiency of available data and/or models on the one hand; and inherent unpredictability of complex social systems on the other. We then use this model to motivate an illustrative empirical study of information cascade size prediction on Twitter. Despite an unprecedented volume of information about users, content, and past performance, our best performing models can explain less than half of the variance in cascade sizes. In turn, this result suggests that even with unlimited data predictive performance would be bounded well below deterministic accuracy. Finally, we explore this potential bound theoretically using simulations of a diffusion process on a random scale free network similar to Twitter. We show that although higher predictive power is possible in theory, such performance requires a homogeneous system and perfect ex-ante knowledge of it: even a small degree of uncertainty in estimating product quality or slight variation in quality across products leads to substantially more restrictive bounds on predictability. We conclude that realistic bounds on predictive accuracy are not dissimilar from those we have obtained empirically, and that such bounds for other complex social systems for which data is more difficult to obtain are likely even lower.
Predicting serves in tennis using style priors
Authors: Xinyu Wei, Patrick Lucey, Stuart Morgan, Peter Carr, Machar Reid, Sridha Sridharan
Venue: Conference KDD 2015
Year: 2015

In professional sport, an enormous amount of fine-grain performance data can be generated at near millisecond intervals in the form of vision-based tracking data. One of the first sports to embrace this technology has been tennis, where Hawk-Eye technology has been used to both aid umpiring decisions, and to visualize shot trajectories for broadcast purposes. These data have tremendous untapped applications in terms of "opponent planning'', where a large amount of recent data is used to learn contextual behavior patterns of individual players, and ultimately predict the likelihood of a particular type of serve. Since the type of serve selected by a player may be contingent on the match context (i.e., is the player down break-point, or is serving for the match etc.), the characteristics of the player (i.e., the player may have a very fast serve, hit heavy with topspin or kick, or slice serves into the body) as well as the characteristics of the opponent (e.g., the opponent may prefer to play from the baseline or "chip-and-charge'' into the net). In this paper we present a method which recommends the most likely serves of a player in a given context. We show by utilizing a "style prior", we can improve the prediction/recommendation. Such an approach also allows us to quantify the similarity between players, which is useful in enriching the dataset for future prediction. We conduct our analysis on Hawk-Eye data collected from three recent Australian Open Grand-Slam Tournaments and show how our approach can be used in practice.
No longer sleeping with a bomb: a duet system for protecting urban safety from dangerous goods
Authors: Jingyuan Wang, Chao Chen, Junjie Wu, Zhang Xiong
Venue: Conference KDD 2017
Year: 2017

Recent years have witnessed the continuous growth of megalopolises worldwide, which makes urban safety a top priority in modern city life. Among various threats, dangerous goods such as gas and hazardous chemicals transported through and around cities have increasingly become the deadly "bomb" we sleep with every day. In both academia and government, tremendous efforts have been dedicated to dealing with dangerous goods transportation (DGT) issues, but further study is still in great need to quantify the problem and explore its intrinsic dynamics in a big data perspective. In this paper, we present a novel system called DGeye, which features a "duet" between DGT trajectory data and human mobility data for risky zones identification. Moreover, DGeye innovatively takes risky patterns as the keystones in DGT management, and builds causality networks among them for pain point identification, attribution and prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye. In particular, the report generated by DGeye driven the Beijing government to lay down gas pipelines for the famous Guijie food street.
Estimating interleaved comparison outcomes from historical click data
Authors: Katja Hofmann, Shimon Whiteson, Maarten de Rijke
Venue: Conference CIKM 2012
Year: 2012

Interleaved comparison methods, which compare rankers using click data, are a promising alternative to traditional information retrieval evaluation methods that require expensive explicit judgments. A major limitation of these methods is that they assume access to live data, meaning that new data must be collected for every pair of rankers compared. We investigate the use of previously collected click data (i.e., historical data) for interleaved comparisons. We start by analyzing to what degree existing interleaved comparison methods can be applied and find that a recent probabilistic method allows such data reuse, even though it is biased when applied to historical data. We then propose an interleaved comparison method that is based on the probabilistic approach but uses importance sampling to compensate for bias. We experimentally confirm that probabilistic methods make the use of historical data for interleaved comparisons possible and effective.
Expected browsing utility for web search evaluation
Authors: Emine Yilmaz, Milad Shokouhi, Nick Craswell, Stephen Robertson
Venue: Conference CIKM 2010
Year: 2010

Most information retrieval evaluation metrics are designed to measure the satisfaction of the user given the results returned by a search engine. In order to evaluate user satisfaction, most of these metrics have underlying user models, which aim at modeling how users interact with search engine results. Hence, the quality of an evaluation metric is a direct function of the quality of its underlying user model. This paper proposes EBU, a new evaluation metric that uses a sophisticated user model tuned by observations over many thousands of real search sessions. We compare EBU with a number of state of the art evaluation metrics and show that it is more correlated with real user behavior captured by clicks.
A content-driven reputation system for the Wikipedia
Authors: B. Thomas Adler, Luca de Alfaro
Venue: Conference WWW 2007
Year: 2007

We present a content-driven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits they perform to Wikipedia articles are preserved by subsequent authors, and they lose reputation when their edits are rolled back or undone in short order. Thus, author reputation is computed solely on the basis of content evolution; user-to-user comments or ratings are not used. The author reputation we compute could be used to flag new contributions from low-reputation authors, or it could be used to allow only authors with high reputation to contribute to controversialor critical pages. A reputation system for the Wikipedia could also provide an incentive for high-quality contributions. We have implemented the proposed system, and we have used it to analyze the entire Italian and French Wikipedias, consisting of a total of 691, 551 pages and 5, 587, 523 revisions. Our results show that our notion of reputation has good predictive value: changes performed by low-reputation authors have a significantly larger than average probability of having poor quality, as judged by human observers, and of being later undone, as measured by our algorithms.
P- Rank: a comprehensive structural similarity measure over information networks
Authors: Peixiang Zhao, Jiawei Han, Yizhou Sun
Venue: Conference CIKM 2009
Year: 2009

With the ubiquity of information networks and their broad applications, the issue of similarity computation between entities of an information network arises and draws extensive research interests. However, to effectively and comprehensively measure "how similar two entities are within an information network" is nontrivial, and the problem becomes even more challenging when the information network to be examined is massive and diverse. In this paper, we propose a new similarity measure, P-Rank (Penetrating Rank), toward effectively computing the structural similarities of entities in real information networks. P-Rank enriches the well-known similarity measure, SimRank, by jointly encoding both in- and out-link relationships into structural similarity computation. P-Rank is proven to be a unified structural similarity framework, under which all state-of-the-art similarity measures, including CoCitation, Coupling, Amsler and SimRank, are just its special cases. Based on its recursive nature of P-Rank, we propose a fixed point algorithm to reinforce structural similarity of vertex pairs beyond the localized neighborhood scope toward the entire information network. Our experimental studies demonstrate the power of P-Rank as an effective similarity measure in different information networks. Meanwhile, under the same time/space complexity, P-Rank outperforms SimRank as a comprehensive and more meaningful structural similarity measure, especially in large real information networks.
Effective audience extension in online advertising
Authors: Jianqiang Shen, Sahin Cem Geyik, Ali Dasdan
Venue: Conference KDD 2015
Year: 2015

In digital advertising, advertisers want to reach the right audience over media channels such as display, mobile, video, or social at the appropriate cost. The right audience for an advertiser consists of existing customers as well as valuable prospects, those that can potentially be turned into future customers. Identifying valuable prospects is called the audience extension problem because advertisers find new customers by extending the desirable criteria for their starting point, which is their existing audience or customers. The complexity of the audience extension problem stems from the difficulty of defining desirable criteria objectively, the number of desirable criteria (such as similarity, diversity, performance) to simultaneously satisfy, and the expected runtime (a few minutes) to find a solution over billions of cookie-based users. In this paper, we formally define the audience extension problem, propose an algorithm that extends a given audience set efficiently under multiple desirable criteria, and experimentally validate its performance. Instead of iterating over individual users, the algorithm takes in Boolean rules that define the seed audience and returns a new set of Boolean rules that corresponds to the extended audience that satisfy the multiple criteria.
Revisiting random binning features: Fast convergence and strong parallelizability
Authors: Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan
Venue: Conference KDD 2016
Year: 2016

Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the number of samples. A number of kernel approximation methods have thus been proposed in the recent years, among which the random features method gains much popularity due to its simplicity and direct reduction of nonlinear problem to a linear one. Different random feature functions have since been proposed to approximate a variety of kernel functions. Among them the Random Binning (RB) feature, proposed in the first random-feature paper [21], has drawn much less attention than the Random Fourier (RF) feature proposed also in [21]. In this work, we observe that the RB features, with right choice of optimization solver, could be orders-of-magnitude more efficient than other random features and kernel approximation methods under the same requirement of accuracy. We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features. In particular, we show that by drawing R random grids with at least κ number of non-empty bins per grid in expectation, RB method achieves a convergence rate of O(1/κ R)), which not only sharpens its O(1/√R) rate from Monte Carlo analysis, but also shows a κ times speedup over other random features under the same analysis framework. In addition, we demonstrate another advantage of RB in the L1-regularized setting, where unlike other random features, a RB-based Coordinate Descent solver can be parallelized with guaranteed speedup proportional to κ. Our extensive experiments demonstrate the superior performance of the RB features over other random features and kernel approximation methods.
Image segmentation by figure-ground composition into maximal cliques
Authors: Adrian Ion, João Carreira 0002, Cristian Sminchisescu
Venue: Conference ICCV 2011
Year: 2011

We propose a mid-level statistical model for image segmentation that composes multiple figure-ground hypotheses (FG) obtained by applying constraints at different locations and scales, into larger interpretations (tilings) of the entire image. Inference is cast as optimization over sets of maximal cliques sampled from a graph connecting all non-overlapping figure-ground segment hypotheses. Potential functions over cliques combine unary, Gestalt-based figure qualities, and pairwise compatibilities among spatially neighboring segments, constrained by T-junctions and the boundary interface statistics of real scenes. Learning the model parameters is based on maximum likelihood, alternating between sampling image tilings and optimizing their potential function parameters. State of the art results are reported on the Berkeley and Stanford segmentation datasets, as well as VOC2009, where a 28% improvement was achieved.
Query Tracking for E- Commerce Conversational Search: A Machine Comprehension Perspective
Authors: Yunlun Yang, Yu Gong, Xi Chen
Venue: Conference CIKM 2018
Year: 2018

With the development of dialog techniques, conversational search has attracted more and more attention as it enables users to interact with the search engine in a natural and efficient manner. However, comparing with the natural language understanding in traditional task-oriented dialog which focuses on slot filling and tracking, the query understanding in E-commerce conversational search is quite different and more challenging due to more diverse user expressions and complex intentions. In this work, we define the real-world problem of query tracking in E-commerce conversational search, in which the goal is to update the internal query after each round of interaction. We also propose a self attention based neural network to handle the task in a machine comprehension perspective. Further more we build a novel E-commerce query tracking dataset from an operational E-commerce Search Engine, and experimental results on this dataset suggest that our proposed model outperforms several baseline methods by a substantial gain for Exact Match accuracy and F1 score, showing the potential of machine comprehension like model for this task.
Mining text snippets for images on the web
Authors: Anitha Kannan, Simon Baker, Krishnan Ramnath, Juliet Fiss, Dahua Lin, Lucy Vanderwende, Rizwan Ansary, Ashish Kapoor, Qifa Ke, Matt Uyttendaele, Xin-Jing Wang, Lei Zhang
Venue: Conference KDD 2014
Year: 2014

Images are often used to convey many different concepts or illustrate many different stories. We propose an algorithm to mine multiple diverse, relevant, and interesting text snippets for images on the web. Our algorithm scales to all images on the web. For each image, all webpages that contain it are considered. The top-K text snippet selection problem is posed as combinatorial subset selection with the goal of choosing an optimal set of snippets that maximizes a combination of relevancy, interestingness, and diversity. The relevancy and interestingness are scored by machine learned models. Our algorithm is run at scale on the entire image index of a major search engine resulting in the construction of a database of images with their corresponding text snippets. We validate the quality of the database through a large-scale comparative study. We showcase the utility of the database through two web-scale applications: (a) augmentation of images on the web as webpages are browsed and (b)~an image browsing experience (similar in spirit to web browsing) that is enabled by interconnecting semantically related images (which may not be visually related) through shared concepts in their corresponding text snippets.