OpenRaiser/Intern-Atlas huggingface dataset:
Implicit elastic matching with random projections for pose-variant face recognition
Authors: John Wright 0001, Gang Hua 0001
Venue: Conference CVPR 2009
Year: 2009

We present a new approach to robust pose-variant face recognition, which exhibits excellent generalization ability even across completely different datasets due to its weak dependence on data. Most face recognition algorithms assume that the face images are very well-aligned. This assumption is often violated in real-life face recognition tasks, in which face detection and rectification have to be performed automatically prior to recognition. Although great improvements have been made in face alignment recently, significant pose variations may still occur in the aligned faces. We propose a multiscale local descriptor-based face representation to mitigate this issue. First, discriminative local image descriptors are extracted from a dense set of multiscale image patches. The descriptors are expanded by their spatial locations. Each expanded descriptor is quantized by a set of random projection trees. The final face representation is a histogram of the quantized descriptors. The location expansion constrains the quantization regions to be localized not just in feature space but also in image space, allowing us to achieve an implicit elastic matching for face images. Our experiments on challenging face recognition benchmarks demonstrate the advantages of the proposed approach for handling large pose variations, as well as its superb generalization ability.
Friend or frenemy?: predicting signed ties in social networks
Authors: Shuang-Hong Yang, Alexander J. Smola, Bo Long, Hongyuan Zha, Yi Chang
Venue: Conference SIGIR 2012
Year: 2012

We study the problem of labeling the edges of a social network graph (e.g., acquaintance connections in Facebook) as either positive (i.e., trust, true friendship) or negative (i.e., distrust, possible frenemy) relations. Such signed relations provide much stronger signal in tying the behavior of online users than the unipolar Homophily effect, yet are largely unavailable as most social graphs only contain unsigned edges.
Tag-based web photo retrieval improved by batch mode re-tagging
Authors: Lin Chen 0021, Dong Xu 0001, Ivor Wai-Hung Tsang, Jiebo Luo 0001
Venue: Conference CVPR 2010
Year: 2010

Web photos in social media sharing websites such as Flickr are generally accompanied by rich but noisy textual descriptions (tags, captions, categories, etc.). In this paper, we proposed a tag-based photo retrieval framework to improve the retrieval performance for Flickr photos by employing a novel batch mode re-tagging method. The proposed batch mode re-tagging method can automatically refine noisy tags of a group of Flickr photos uploaded by the same user within a short period by leveraging millions of training web images and their associated rich textual descriptions. Specifically, for one group of Flickr photos, we construct a group-specific lexicon which contains only the tags of all photos within the group. For each query tag, we employ the inverted file method to automatically find loosely labeled training web images. We propose a SVM with Augmented Features, referred to as AFSVM, to learn adapted classifiers to refine the annotation tags of photos by leveraging the existing SVM classifiers of popular tags, which are associated with a large amount of positive training web images. Moreover, to further refine the annotation tags of photos in the same group, we additionally introduce an objective function that utilizes the visual similarities of photos within the group as well as the semantic proximities of their tags. Based on the refined tags, photos can be retrieved according to more reliable relevance scores. Extensive experiments demonstrate the effectiveness of our framework.
Exploiting ranking factorization machines for microblog retrieval
Authors: Runwei Qiang, Feng Liang, Jianwu Yang
Venue: Conference CIKM 2013
Year: 2013

Learning to rank method has been proposed for practical application in the field of information retrieval. When employing it in microblog retrieval, the significant interactions of various involved features are rarely considered. In this paper, we propose a Ranking Factorization Machine (Ranking FM) model, which applies Factorization Machine model to microblog ranking on basis of pairwise classification. In this way, our proposed model combines the generality of learning to rank framework with the advantages of factorization models in estimating interactions between features, leading to better retrieval performance. Moreover, three groups of features (content relevance features, semantic expansion features and quality features) and their interactions are utilized in the Ranking FM model with the methods of stochastic gradient descent and adaptive regularization for optimization. Experimental results demonstrate its superiority over several baseline systems on a real Twitter dataset in terms of P@30 and MAP metrics. Furthermore, it outperforms the best performing results in the TREC'12 Real-Time Search Task.
The logical diversity of explanations in OWL ontologies
Authors: Samantha Bail, Bijan Parsia, Ulrike Sattler
Venue: Conference CIKM 2013
Year: 2013

Given the high expressivity of the Web Ontology Language OWL 2, there is a potential for great diversity in the logical content of OWL ontologies. The fact that many naturally occurring entailments of such ontologies have multiple justifications indicates that ontologies often overdetermine their consequences, suggesting a diversity in supporting reasons. On closer inspection, however, we often find that justifications-even for multiple entailments-appear to be structurally similar, suggesting that their multiplicity might be due to diverse material, not formal grounds for an entailment.
Probabilistic group-level motion analysis and scenario recognition
Authors: Ming-Ching Chang, Nils Krahnstoever, Weina Ge
Venue: Conference ICCV 2011
Year: 2011

This paper addresses the challenge of recognizing behavior of groups of individuals in unconstraint surveillance environments. As opposed to approaches that rely on agglomerative or decisive hierarchical clustering techniques, we propose to recognize group interactions without making hard decisions about the underlying group structure. Instead we use a probabilistic grouping strategy evaluated from the pairwise spatial-temporal tracking information. A path-based grouping scheme determines a soft segmentation of groups and produces a weighted connection graph where its edges express the probability of individuals belonging to a group. Without further segmenting this graph, we show how a large number of low- and high-level behavior recognition tasks can be performed. Our work builds on a mature multi-camera multi-target person tracking system that operates in real-time. We derive probabilistic models to analyze individual track motion as well as group interactions. We show that the soft grouping can combine with motion analysis elegantly to robustly detect and predict group-level activities. Experimental results demonstrate the efficacy of our approach.
A transfer learning based framework of crowd-selection on twitter
Authors: Zhou Zhao, Da Yan, Wilfred Ng, Shi Gao
Venue: Conference KDD 2013
Year: 2013

Crowd selection is essential to crowd sourcing applications, since choosing the right workers with particular expertise to carry out crowdsourced tasks is extremely important. The central problem is simple but tricky: given a crowdsourced task, who are the most knowledgable users to ask? In this demo, we show our framework that tackles the problem of crowdsourced task assignment on Twitter according to the social activities of its users. Since user profiles on Twitter do not reveal user interests and skills, we transfer the knowledge from categorized Yahoo! Answers datasets for learning user expertise. Then, we select the right crowd for certain tasks based on user expertise. We study the effectiveness of our system using extensive user evaluation. We further engage the attendees to participate a game called--Whom to Ask on Twitter?. This helps understand our ideas in an interactive manner. Our crowd selection can be accessed by the following url http://webproject2.cse.ust.hk:8034/tcrowd/.
Regression from local features for viewpoint and pose estimation
Authors: Marwan Torki, Ahmed M. Elgammal
Venue: Conference ICCV 2011
Year: 2011

In this paper we propose a framework for learning a regression function form a set of local features in an image. The regression is learned from an embedded representation that reflects the local features and their spatial arrangement as well as enforces supervised manifold constraints on the data. We applied the approach for viewpoint estimation on a Multiview car dataset, a head pose dataset and arm posture dataset. The experimental results show that this approach has superior results (up to 67% improvement) to the state-of-the-art approaches in very challenging datasets.
Clustering Appearance for Scene Analysis
Authors: Sanjeev J. Koppal, Srinivasa G. Narasimhan
Venue: Conference CVPR 2006
Year: 2006

We propose a new approach called "appearance clustering" for scene analysis. The key idea in this approach is that the scene points can be clustered according to their surface normals, even when the geometry, material and lighting are all unknown. We achieve this by analyzing an image sequence of a scene as it is illuminated by a smoothly moving distant source. Each pixel thus gives rise to a "continuous appearance profile" that yields information about derivatives of the BRDF w.r.t source direction. This information is directly related to the surface normal of the scene point when the source path follows an unstructured trajectory (obtained, say, by "hand-waving"). Based on this observation, we transform the appearance profiles and propose a metric that can be used with any unsupervised clustering algorithm to obtain iso-normal clusters. We successfully demonstrate appearance clustering for complex indoor and outdoor scenes. In addition, iso-normal clusters serve as excellent priors for scene geometry and can strongly impact any vision algorithm that attempts to estimate material, geometry and/or lighting properties in a scene from images. We demonstrate this impact for applications such as diffuse and specular separation, both calibrated and uncalibrated photometric stereo of non-lambertian scenes, light source estimation and texture transfer.
Modeling the impact of lifestyle on health at scale
Authors: Adam Sadilek, Henry A. Kautz
Venue: Conference WSDM 2013
Year: 2013

Research in computational epidemiology to date has concentrated on estimating summary statistics of populations and simulated scenarios of disease outbreaks. Detailed studies have been limited to small domains, as scaling the methods involved poses considerable challenges. By contrast, we model the associations of a large collection of social and environmental factors with the health of particular individuals. Instead of relying on surveys, we apply scalable machine learning techniques to noisy data mined from online social media and infer the health state of any given person in an automated way. We show that the learned patterns can be subsequently leveraged in descriptive as well as predictive fine-grained models of human health. Using a unified statistical model, we quantify the impact of social status, exposure to pollution, interpersonal interactions, and other important lifestyle factors on one's health. Our model explains more than 54% of the variance in people's health (as estimated from their online communication), and predicts the future health status of individuals with 91% accuracy. Our methods complement traditional studies in life sciences, as they enable us to perform large-scale and timely measurement, inference, and prediction of previously elusive factors that affect our everyday lives.
Data-driven multi-touch attribution models
Authors: Xuhui Shao, Lexin Li
Venue: Conference KDD 2011
Year: 2011

In digital advertising, attribution is the problem of assigning credit to one or more advertisements for driving the user to the desirable actions such as making a purchase. Rather than giving all the credit to the last ad a user sees, multi-touch attribution allows more than one ads to get the credit based on their corresponding contributions. Multi-touch attribution is one of the most important problems in digital advertising, especially when multiple media channels, such as search, display, social, mobile and video are involved. Due to the lack of statistical framework and a viable modeling approach, true data-driven methodology does not exist today in the industry. While predictive modeling has been thoroughly researched in recent years in the digital advertising domain, the attribution problem focuses more on accurate and stable interpretation of the influence of each user interaction to the final user decision rather than just user classification. Traditional classification models fail to achieve those goals.
Targeting direct cash transfers to the extremely poor
Authors: Brian Abelson, Kush R. Varshney, Joy Sun
Venue: Conference KDD 2014
Year: 2014

Unconditional cash transfers to the extreme poor via mobile telephony represent a radical, new approach to giving. GiveDirectly is a non-governmental organization (NGO) at the vanguard of delivering this proven and effective approach to reducing poverty. In this work, we streamline an important step in the operations of the NGO by developing and deploying a data-driven system for locating villages with extreme poverty in Kenya and Uganda. Using the type of roof of a home, thatched or metal, as a proxy for poverty, we develop a new remote sensing approach for selecting extremely poor villages to target for cash transfers. We develop an analytics algorithm that estimates housing quality and density in patches of publicly-available satellite imagery by learning a predictive model with sieves of template matching results combined with color histograms as features. We develop and deploy a crowdsourcing interface to obtain labeled training data. We deploy the predictive model to construct a fine-scale heat map of poverty and integrate this discovered knowledge into the processes of GiveDirectly's operations. Aggregating estimates at the village level, we produce a ranked list from which top villages are included in GiveDirectly's planned distribution of cash transfers. The automated approach increases village selection efficiency significantly.
Supervised translation­ invariant sparse coding
Authors: Jianchao Yang, Kai Yu 0001, Thomas S. Huang
Venue: Conference CVPR 2010
Year: 2010

In this paper, we propose a novel supervised hierarchical sparse coding model based on local image descriptors for classification tasks. The supervised dictionary training is performed via back-projection, by minimizing the training error of classifying the image level features, which are extracted by max pooling over the sparse codes within a spatial pyramid. Such a max pooling procedure across multiple spatial scales offer the model translation invariant properties, similar to the Convolutional Neural Network (CNN). Experiments show that our supervised dictionary improves the performance of the proposed model significantly over the unsupervised dictionary, leading to state-of-the-art performance on diverse image databases. Further more, our supervised model targets learning linear features, implying its great potential in handling large scale datasets in real applications.
Optimizing budget constrained spend in search advertising
Authors: Chinmay Karande, Aranyak Mehta, Ramakrishnan Srikant
Venue: Conference WSDM 2013
Year: 2013

Search engine ad auctions typically have a significant fraction of advertisers who are budget constrained, i.e., if allowed to participate in every auction that they bid on, they would spend more than their budget. This yields an important problem: selecting the ad auctions which these advertisers participate, in order to optimize different system objectives such as the return on investment for advertisers, and the quality of ads shown to users. We present a system and algorithms for optimizing budget constrained spend. The system is designed be deployed in a large search engine, with hundreds of thousands of advertisers, millions of searches per hour, and with the query stream being only partially predictable. We have validated the system design by implementing it in the Google ads serving system and running experiments on live traffic. We have also compared our algorithm to previous work that casts this problem as a large linear programming problem limited to popular queries, and show that our algorithms yield substantially better results.
Lead-lag analysis via sparse co-projection in correlated text streams
Authors: Fangzhao Wu, Yangqiu Song, Shixia Liu, Yongfeng Huang, Zhenyu Liu
Venue: Conference CIKM 2013
Year: 2013

Correlated topical trend detection is very useful in analyzing public and social media influence. In this paper, we propose an algorithm that can both detect the correlation and discover the corresponding keywords that trigger the correlation. To detect the correlation, we use a projection vector to project two text streams onto the same space, and then use a least square cost function to regress one text stream over the other with different time lags. To extract the corresponding keywords, we impose the non-negative sparsity constraints over the projection parameters. In addition, we present an accelerated algorithm based on Nesterov's method to efficiently solve the optimization problem. In our experiments, we use both syntehtic and real data sets to demonstrate the advantages and capabilities of the proposed algorithm over CCA on the follower link prediction problem.
Querying Graphs with Preferences
Authors: Valeria Fionda, Giuseppe Pirrò
Venue: Conference CIKM 2013
Year: 2013

This paper presents GuLP a graph query language that enables to declaratively express preferences. Preferences enable to order the answers to a query and can be stated in terms of nodes/edge attributes and complex paths. We present the formal syntax and semantics of GuLP and a polynomial time algorithm for evaluating GuLP expressions. We describe an implementation of GuLP in the GuLP-it system, which is available for download. We evaluate the GuLP-it system on real-world and synthetic data.
Statistical models of music-listening sessions in social media
Authors: Elena Zheleva, John Guiver, Eduarda Mendes Rodrigues, Natasa Milic-Frayling
Venue: Conference WWW 2010
Year: 2010

User experience in social media involves rich interactions with the media content and other participants in the community. In order to support such communities, it is important to understand the factors that drive the users' engagement. In this paper we show how to define statistical models of different complexity to describe patterns of song listening in an online music community. First, we adapt the LDA model to capture music taste from listening activities across users and identify both the groups of songs associated with the specific taste and the groups of listeners who share the same taste. Second, we define a graphical model that takes into account listening sessions and captures the listening moods of users in the community. Our session model leads to groups of songs and groups of listeners with similar behavior across listening sessions and enables faster inference when compared to the LDA model. Our experiments with the data from an online media site demonstrate that the session model is better in terms of the perplexity compared to two other models: the LDA-based taste model that does not incorporate cross-session information and a baseline model that does not use latent groupings of songs.
Finding the weakest link in person detectors
Authors: Devi Parikh, C. Lawrence Zitnick
Venue: Conference CVPR 2011
Year: 2011

Detecting people remains a popular and challenging problem in computer vision. In this paper, we analyze parts-based models for person detection to determine which components of their pipeline could benefit the most if improved. We accomplish this task by studying numerous detectors formed from combinations of components performed by human subjects and machines. The parts-based model we study can be roughly broken into four components: feature detection, part detection, spatial part scoring and contextual reasoning including non-maximal suppression. Our experiments conclude that part detection is the weakest link for challenging person detection datasets. Non-maximal suppression and context can also significantly boost performance. However, the use of human or machine spatial models does not significantly or consistently affect detection accuracy.
Coupled detection and trajectory estimation for multi-object tracking
Authors: Bastian Leibe, Konrad Schindler, Luc Van Gool
Venue: Conference ICCV 2007
Year: 2007

We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. It is formulated in a hypothesis selection framework and builds upon a state-of-the-art pedestrian detector. At each time instant, it searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and for all evidence collected so far, while satisfying the constraints that no two objects may occupy the same physical space, nor explain the same image pixels at any point in time. Successful trajectory hypotheses are fed back to guide object detection in future frames. The optimization procedure is kept efficient through incremental computation and conservative hypothesis pruning. The resulting approach can initialize automatically and track a large and varying number of persons over long periods and through complex scenes with clutter, occlusions, and large-scale background changes. Also, the global optimization framework allows our system to recover from mismatches and temporarily lost tracks. We demonstrate the feasibility of the proposed approach on several challenging video sequences.
FACTO: a fact lookup engine based on web tables
Authors: Xiaoxin Yin, Wenzhao Tan, Chao Liu
Venue: Conference WWW 2011
Year: 2011

Recently answers for fact lookup queries have appeared on major search engines. For example, for the query Barack Obama date of birth Google directly shows "4 August 1961" above its regular results. In this paper, we describe FACTO, an end-to-end system for answering fact lookup queries for web search. FACTO extracts structured data from tables on the web, aggregates and cleans such data and stores them in a database. Given a web search query, FACTO will decide if it asks for facts in this database, and provides the most confident answer when possible. FACTO achieves higher precision and comparable coverage comparing with the fact lookup engines by Google and Ask.com, although FACTO is developed by a very small team. We present the challenges and our solutions in developing every component of FACTO. Some solutions are based on existing technologies, and many others are novel approaches proposed by us.