OpenRaiser/Intern-Atlas huggingface dataset:
Estimating 3d shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior
Authors: Sami Romdhani, Thomas Vetter
Venue: Conference CVPR 2005
Year: 2005

We present a novel algorithm aiming to estimate the 3D shape, the texture of a human face, along with the 3D pose and the light direction from a single photograph by recovering the parameters of a 3D morphable model. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the estimation process. This was previously achieved using either a simple model, such as the Lambertian reflectance model, leading to a linear fitting algorithm. Alternatively, this problem was addressed using a more precise model and minimizing a non-convex cost function with many local minima. One way to reduce the local minima problem is to use a stochastic optimization algorithm. However, the convergence properties (such as the radius of convergence) of such algorithms, are limited. Here, as well as the pixel intensity, we use various image features such as the edges or the location of the specular highlights. The 3D shape, texture and imaging parameters are then estimated by maximizing the posterior of the parameters given these image features. The overall cost function obtained is smoother and, hence, a stochastic optimization algorithm is not needed to avoid the local minima problem. This leads to the multi-features fitting algorithm that has a wider radius of convergence and a higher level of precision. This is shown on some example photographs, and on a recognition experiment performed on the CMU-PIE image database.
Characterizing, detecting, and predicting online ban evasion
Authors: Manoj Niverthi, Gaurav Verma, Srijan Kumar
Venue: Conference WWW 2022
Year: 2022

Moderators and automated methods enforce bans on malicious users who engage in disruptive behavior. However, malicious users can easily create a new account to evade such bans. Previous research has focused on other forms of online deception, like the simultaneous operation of multiple accounts by the same entities (sockpuppetry), impersonation of other individuals, and studying the effects of de-platforming individuals and communities. Here we conduct the first data-driven study of ban evasion, i.e., the act of circumventing bans on an online platform, leading to temporally disjoint operation of accounts by the same user. We curate a novel dataset of 8,551 ban evasion pairs (parent, child) identified on Wikipedia and contrast their behavior with benign users and non-evading malicious users. We find that evasion child accounts demonstrate similarities with respect to their banned parent accounts on several behavioral axes - from similarity in usernames and edited pages to similarity in content added to the platform and its psycholinguistic attributes. We reveal key behavioral attributes of accounts that are likely to evade bans. Based on the insights from the analyses, we train logistic regression classifiers to detect and predict ban evasion at three different points in the ban evasion lifecycle. Results demonstrate the effectiveness of our methods in predicting future evaders (AUC = 0.78), early detection of ban evasion (AUC = 0.85), and matching child accounts with parent accounts (MRR = 0.97). Our work can aid moderators by reducing their workload and identifying evasion pairs faster and more efficiently than current manual and heuristic-based approaches. Dataset is available https://github.com/srijankr/ban_evasion.
Rank List Sensitivity of Recommender Systems to Interaction Perturbations
Authors: Sejoon Oh, Berk Ustun, Julian J. McAuley, Srijan Kumar
Venue: Conference CIKM 2022
Year: 2022

Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual data points during test time. In this work, we study this sensitivity in recommender systems, where users' recommendations are drastically altered by minor perturbations in other unrelated users' interactions. We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data. We develop a method, CASPER, which uses cascading effect to identify the minimal and systematical perturbation to induce higher instability in a recommender system. Experiments on four datasets show that recommender models are overly sensitive to minor perturbations introduced randomly or via CASPER - even perturbing one random interaction of one user drastically changes the recommendation lists of all users.Importantly, with CASPER perturbation, the models generate more unstable recommend ations for low-accuracy users (i.e., those who receive low-quality recommendations) than high-accuracy ones.
Consistent surface color for texturing large objects in outdoor scenes
Authors: Rei Kawakami, Katsushi Ikeuchi, Robby T. Tan
Venue: Conference ICCV 2005
Year: 2005

Color appearance of an object is significantly influenced by the color of the illumination. When the illumination color changes, the color appearance of the object change accordingly, causing its appearance to be inconsistent. To arrive at color constancy, we have developed a physics-based method of estimating and removing the illumination color. In this paper, we focus on the use of this method to deal with outdoor scenes, since very few physics-based methods have successfully handled outdoor color constancy. Our method is principally based on shadowed and non-shadowed regions. Previously researchers have discovered that shadowed regions are illuminated by sky light, while non-shadowed regions are illuminated by a combination of sky light and sunlight. Based on this difference of illumination, we estimate the illumination colors (both the sunlight and the sky light) and then remove them. To reliably estimate the illumination colors in outdoor scenes, we include the analysis of noise, since the presence of noise is inevitable in natural images. As a result, compared to existing methods, the proposed method is more effective and robust in handling outdoor scenes. In addition, the proposed method requires only a single input image, making it useful for many applications of computer vision
Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction
Authors: Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu
Venue: Conference WSDM 2018
Year: 2018

Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.
From one tree to a forest: a unified solution for structured web data extraction
Authors: Qiang Hao, Rui Cai, Yanwei Pang, Lei Zhang
Venue: Conference SIGIR 2011
Year: 2011

Structured data, in the form of entities and associated attributes, has been a rich web resource for search engines and knowledge databases. To efficiently extract structured data from enormous websites in various verticals (e.g., books, restaurants), much research effort has been attracted, but most existing approaches either require considerable human effort or rely on strong features that lack of flexibility. We consider an ambitious scenario -- can we build a system that (1) is general enough to handle any vertical without re-implementation and (2) requires only one labeled example site from each vertical for training to automatically deal with other sites in the same vertical? In this paper, we propose a unified solution to demonstrate the feasibility of this scenario. Specifically, we design a set of weak but general features to characterize vertical knowledge (including attribute-specific semantics and inter-attribute layout relationships). Such features can be adopted in various verticals without redesign; meanwhile, they are weak enough to avoid overfitting of the learnt knowledge to seed sites. Given a new unseen site, the learnt knowledge is first applied to identify page-level candidate attribute values, while inevitably involve false positives. To remove noise, site-level information of the new site is then exploited to boost up the true values. The site-level information is derived in an unsupervised manner, without harm to the applicability of the solution. Promising experimental performance on 80 websites in 8 distinct verticals demonstrated the feasibility and flexibility of the proposed solution.
Pre-training Molecular Graph Representation with 3D Geometry
Authors: Shengchao Liu, Hanchen Wang 0002, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang 0005
Venue: Conference ICLR 2022
Year: 2022

Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically modeled by their 2D topological structures, but it has been recently discovered that 3D geometric information plays a more vital role in predicting molecular functionalities. However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation. To cope with this challenge, we propose the Graph Multi-View Pre-training (GraphMVP) framework where self-supervised learning (SSL) is performed by leveraging the correspondence and consistency between 2D topological structures and 3D geometric views. GraphMVP effectively learns a 2D molecular graph encoder that is enhanced by richer and more discriminative 3D geometry. We further provide theoretical insights to justify the effectiveness of GraphMVP. Finally, comprehensive experiments show that GraphMVP can consistently outperform existing graph SSL methods.
Beyond triplets: Hyper-relational knowledge graph embedding for link prediction
Authors: Paolo Rosso, Dingqi Yang, Philippe Cudré-Mauroux
Venue: Conference WWW 2020
Year: 2020

Knowledge Graph (KG) embeddings are a powerful tool for predicting missing links in KGs. Existing techniques typically represent a KG as a set of triplets, where each triplet (h, r, t) links two entities h and t through a relation r, and learn entity/relation embeddings from such triplets while preserving such a structure. However, this triplet representation oversimplifies the complex nature of the data stored in the KG, in particular for hyper-relational facts, where each fact contains not only a base triplet (h, r, t), but also the associated key-value pairs (k, v). Even though a few recent techniques tried to learn from such data by transforming a hyper-relational fact into an n-ary representation (i.e., a set of key-value pairs only without triplets), they result in suboptimal models as they are unaware of the triplet structure, which serves as the fundamental data structure in modern KGs and preserves the essential information for link prediction. To address this issue, we propose HINGE, a hyper-relational KG embedding model, which directly learns from hyper-relational facts in a KG. HINGE captures not only the primary structural information of the KG encoded in the triplets, but also the correlation between each triplet and its associated key-value pairs. Our extensive evaluation shows the superiority of HINGE on various link prediction tasks over KGs. In particular, HINGE consistently outperforms not only the KG embedding methods learning from triplets only (by 0.81-41.45% depending on the link prediction tasks and settings), but also the methods learning from hyper-relational facts using the n-ary representation (by 13.2-84.1%).
Multitask mixture of sequential experts for user activity streams
Authors: Zhen Qin 0001, Yicheng Cheng, Zhe Zhao 0001, Zhe Chen, Donald Metzler, Jingzheng Qin
Venue: Conference KDD 2020
Year: 2020

It is often desirable to model multiple objectives in real-world web applications, such as user satisfaction and user engagement in recommender systems. Multi-task learning has become the standard approach for such applications recently.
Visbert: Hidden-state visualizations for transformers
Authors: Betty van Aken, Benjamin Winter, Alexander Löser, Felix A. Gers
Venue: Conference WWW 2020
Year: 2020

Explainability and interpretability are two important concepts, the absence of which can and should impede the application of well-performing neural networks to real-world problems. At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude of NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) is one such black-box model. It has become a staple architecture to solve many different NLP tasks and has inspired a number of related Transformer models. Understanding how these models draw conclusions is crucial for both their improvement and application. We contribute to this challenge by presenting VisBERT, a tool for visualizing the contextual token representations within BERT for the task of (multi-hop) Question Answering. Instead of analyzing attention weights, we focus on the hidden states resulting from each encoder block within the BERT model. This way we can observe how the semantic representations are transformed throughout the layers of the model. VisBERT enables users to get insights about the model’s internal state and to explore its inference steps or potential shortcomings. The tool allows us to identify distinct phases in BERT’s transformations that are similar to a traditional NLP pipeline and offer insights during failed predictions.
The offset tree for learning with partial labels
Authors: Alina Beygelzimer, John Langford
Venue: Conference KDD 2009
Year: 2009

We present an algorithm, called the Offset Tree, for learning to make decisions in situations where the payoff of only one choice is observed, rather than all choices. The algorithm reduces this setting to binary classification, allowing one to reuse any existing, fully supervised binary classification algorithm in this partial information setting. We show that the Offset Tree is an optimal reduction to binary classification. In particular, it has regret at most (k-1) times the regret of the binary classifier it uses (where k is the number of choices), and no reduction to binary classification can do better. This reduction is also computationally optimal, both at training and test time, requiring just O(log2 k) work to train on an example or make a prediction. Experiments with the Offset Tree show that it generally performs better than several alternative approaches.
IGLUE: A benchmark for transfer learning across modalities, tasks, and languages
Authors: Emanuele Bugliarello, Fangyu Liu 0001, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo Maria Ponti, Ivan Vulic
Venue: Conference ICML 2022
Year: 2022

Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together - by both aggregating pre-existing datasets and creating new ones - visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups. Based on the evaluation of the available state-of-the-art models, we find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks. Moreover, downstream performance is partially explained by the amount of available unlabelled textual data for pretraining, and only weakly by the typological distance of target-source languages. We hope to encourage future research efforts in this area by releasing the benchmark to the community.
Poselets: Body part detectors trained using 3d human pose annotations
Authors: Lubomir D. Bourdev, Jitendra Malik
Venue: Conference ICCV 2009
Year: 2009

We address the classic problems of detection, segmentation and pose estimation of people in images with a novel definition of a part, a poselet. We postulate two criteria (1) It should be easy to find a poselet given an input image (2) it should be easy to localize the 3D configuration of the person conditioned on the detection of a poselet. To permit this we have built a new dataset, H3D, of annotations of humans in 2D photographs with 3D joint information, inferred using anthropometric constraints. This enables us to implement a data-driven search procedure for finding poselets that are tightly clustered in both 3D joint configuration space as well as 2D image appearance. The algorithm discovers poselets that correspond to frontal and profile faces, pedestrians, head and shoulder views, among others. Each poselet provides examples for training a linear SVM classifier which can then be run over the image in a multiscale scanning mode. The outputs of these poselet detectors can be thought of as an intermediate layer of nodes, on top of which one can run a second layer of classification or regression. We show how this permits detection and localization of torsos or keypoints such as left shoulder, nose, etc. Experimental results show that we obtain state of the art performance on people detection in the PASCAL VOC 2007 challenge, among other datasets. We are making publicly available both the H3D dataset as well as the poselet parameters for use by other researchers.
Neural mathematical solver with enhanced formula structure
Authors: Zhenya Huang, Qi Liu 0003, Weibo Gao, Jinze Wu, Yu Yin 0002, Hao Wang 0076, Enhong Chen
Venue: Conference SIGIR 2020
Year: 2020

Automatically answering mathematical problems is a challenging task since it requires not only the ability of linguistic understanding but also mathematical comprehension. Existing studies usually explore solutions on the elementary math word problems that aim to understand the questions described in natural language narratives, which are not capable of solving more general problems containing structural formulas. To this end, in this paper, we propose a novel Neural Mathematical Solver (NMS) with enhanced formula structures. Specifically, we first frame the formulas in a certain problem as a TeX dependency graph to preserve formula-enriched structures. Then, we design a formula graph network (FGN) to capture its mathematical relations. Next, we develop a novel architecture with two GRU models, connecting tokens from both word space and formula space together, to learn the linguistic semantics for the answers. Extensive experiments on a large-scale dataset demonstrate that NMS not only achieves better answer prediction but also visualizes reasonable mathematical representations of problems.
Content recommendation for viral social influence
Authors: Sergei Ivanov, Konstantinos Theocharidis, Manolis Terrovitis, Panagiotis Karras
Venue: Conference SIGIR 2017
Year: 2017

How do we create content that will become viral in a whole network after we share it with friends or followers' Significant research activity has been dedicated to the problem of strategically selecting a seed set of initial adopters so as to maximize a meme's spread in a network. This line of work assumes that the success of such a campaign depends solely on the choice of a tunable seed set of adopters, while the way users perceive the propagated meme is fixed. Yet, in many real-world settings, the opposite holds: a meme's propagation depends on users' perceptions of its tunable characteristics, while the set of initiators is fixed.
Geodemographic influence maximization
Authors: Kaichen Zhang, Jingbo Zhou 0003, Donglai Tao, Panagiotis Karras, Qing Li 0001, Hui Xiong 0001
Venue: Conference KDD 2020
Year: 2020

Given a set of locations in a city, on which ones should we place ads on so as to reach as many people as possible within a limited budget? Past research has addressed this question under the assumption that dense trajectory data are available to determine the reach of each ad. However, the data that are available in most industrial settings do not consist of dense, long-range trajectories; instead, they consist of statistics on people's short-range point-to-point movements. In this paper, we address the natural problem that arises such data: given a distribution of population and point-to-point movement statistics over a network, find a set of locations within a budget that achieves maximum expected reach. We call this problem geodemographic influence maximization (GIM). We show that the problem is NP-hard, but its objective function is monotone and submodular, thus admits a greedy algorithm with a 1 over 2 (1-1 over e) approximation ratio. Still, this algorithm is inapplicable on large-scale data for high-frequency digital signage ads. We develop an efficient deterministic algorithm, Lazy-Sower, exploiting a novel, tight double-bounding scheme of marginal influence gain as well as the locality proprieties of the problem; a learning-based variant, NN-Sower, utilizes randomization and deep learning to further improve efficiency, with a slight loss of quality. Our exhaustive experimental study on two real-world urban datasets demonstrates the efficacy and efficiency of our solutions compared to baselines.
Decoupling Representation and Classifier for Long-Tailed Recognition
Authors: Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan 0001, Albert Gordo, Jiashi Feng, Yannis Kalantidis
Venue: Conference ICLR 2020
Year: 2020

The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g., by loss re-weighting, data re-sampling, or transfer learning from head- to tail-classes, but most of them adhere to the scheme of jointly learning representations and classifiers. In this work, we decouple the learning procedure into representation learning and classification, and systematically explore how different balancing strategies affect them for long-tailed recognition. The findings are surprising: (1) data imbalance might not be an issue in learning high-quality representations; (2) with representations learned with the simplest instance-balanced (natural) sampling, it is also possible to achieve strong long-tailed recognition ability by adjusting only the classifier. We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like ImageNet-LT, Places-LT and iNaturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification. Our code is available at https://github.com/facebookresearch/classifier-balancing.