OpenRaiser/Intern-Atlas huggingface dataset:
GPCA: An efficient dimension reduction scheme for image compression and retrieval
Authors: Jieping Ye, Ravi Janardan, Qi Li
Venue: Conference KDD 2004
Year: 2004

Recent years have witnessed a dramatic increase in the quantity of image data collected, due to advances in fields such as medical imaging, reconnaissance, surveillance, astronomy, multimedia etc. With this increase has come the need to be able to store, transmit, and query large volumes of image data efficiently. A common operation on image databases is the retrieval of all images that are similar to a query image. For this, the images in the database are often represented as vectors in a high-dimensional space and a query is answered by retrieving all image vectors that are proximal to the query image in this space, under a suitable similarity metric. To overcome problems associated with high dimensionality, such as high storage and retrieval times, a dimension reduction step is usually applied to the vectors to concentrate relevant information in a small number of dimensions. Principal Component Analysis (PCA) is a well-known dimension reduction scheme. However, since it works with vectorized representations of images, PCA does not take into account the spatial locality of pixels in images. In this paper, a new dimension reduction scheme, called Generalized Principal Component Analysis (GPCA), is presented. This scheme works directly with images in their native state, as two-dimensional matrices, by projecting the images to a vector space that is the tensor product of two lower-dimensional vector spaces. Experiments on databases of face images show that, for the same amount of storage, GPCA is superior to PCA in terms of quality of the compressed images, query precision, and computational cost.
Microphone arrays as generalized cameras for integrated audio visual processing
Authors: Adam O&apos, Donovan, Ramani Duraiswami, Jan Neumann
Venue: Conference CVPR 2007
Year: 2007

Combinations of microphones and cameras allow the joint audio visual sensing of a scene. Such arrangements of sensors are common in biological organisms and in applications such as meeting recording and surveillance where both modalities are necessary to provide scene understanding. Microphone arrays provide geometrical information on the source location, and allow the sound sources in the scene to be separated and the noise suppressed, while cameras allow the scene geometry and the location and motion of people and other objects to be estimated. In most previous work the fusion of the audio-visual information occurs at a relatively late stage. In contrast, we take the viewpoint that both cameras and microphone arrays are geometry sensors, and treat the microphone arrays as generalized cameras. We employ computer-vision inspired algorithms to treat the combined system of arrays and cameras. In particular, we consider the geometry introduced by a general microphone array and spherical microphone arrays. The latter show a geometry that is very close to central projection cameras, and we show how standard vision based calibration algorithms can be profitably applied to them. Experiments are presented that demonstrate the usefulness of the considered approach.
Blind haze separation
Authors: Sarit Shwartz, Einav Namer, Yoav Y. Schechner
Venue: Conference CVPR 2006
Year: 2006

Outdoor imaging is plagued by poor visibility conditions due to atmospheric scattering, particularly in haze. A major problem is spatially-varying reduction of contrast by stray radiance (airlight), which is scattered by the haze particles towards the camera. Recent computer vision methods have shown that images can be compensated for haze, and even yield a depth map of the scene. A key step in such a scene recovery is subtraction of the airlight. In particular, this can be achieved by analyzing polarization-filtered images. However, the recovery requires parameters of the airlight. These parameters were estimated in past studies by measuring pixels in sky areas. This paper derives an approach for blindly recovering the parameter needed for separating the airlight from the measurements, thus recovering contrast, with neither user interaction nor existence of the sky in the frame. This eases the interaction and conditions needed for image dehazing, which also requires compensation for attenuation. The approach has proved successful in experiments, some of which are shown here.
Perceptually- Inspired and Edge-Directed Color Image Super-Resolution
Authors: Yu-Wing Tai, Wai-Shun Tong, Chi-Keung Tang
Venue: Conference CVPR 2006
Year: 2006

Inspired by multi-scale tensor voting, a computational framework for perceptual grouping and segmentation, we propose an edge-directed technique for color image superresolution given a single low-resolution color image. Our multi-scale technique combines the advantages of edgedirected, reconstruction-based and learning-based methods, and is unique in two ways. First, we consider simultaneously all the three color channels in our multi-scale tensor voting framework to produce a multi-scale edge representation to guide the process of high-resolution color image reconstruction, which is subject to the back projection constraint. Fine details are inferred without noticeable blurry or ringing artifacts. Second, the inference of highresolution curves is achieved by multi-scale tensor voting, using the dense voting field as an edge-preserving smoothness prior which is derived geometrically without any timeconsuming learning procedure. Qualitative and quantitative results indicate that our method produces convincing results in complex test cases typically used by state-of-theart image super-resolution techniques.
Real-time scene text localization and recognition
Authors: Lukás Neumann, Jiri Matas
Venue: Conference CVPR 2012
Year: 2012

An end-to-end real-time scene text localization and recognition method is presented. The real-time performance is achieved by posing the character detection problem as an efficient sequential selection from the set of Extremal Regions (ERs). The ER detector is robust to blur, illumination, color and texture variation and handles low-contrast text. In the first classification stage, the probability of each ER being a character is estimated using novel features calculated with O(1) complexity per region tested. Only ERs with locally maximal probability are selected for the second stage, where the classification is improved using more computationally expensive features. A highly efficient exhaustive search with feedback loops is then applied to group ERs into words and to select the most probable character segmentation. Finally, text is recognized in an OCR stage trained using synthetic fonts. The method was evaluated on two public datasets. On the ICDAR 2011 dataset, the method achieves state-of-the-art text localization results amongst published methods and it is the first one to report results for end-to-end text recognition. On the more challenging Street View Text dataset, the method achieves state-of-the-art recall. The robustness of the proposed method against noise and low contrast of characters is demonstrated by “false positives” caused by detected watermark text in the dataset.
Illumination multiplexing within fundamental limits
Authors: Netanel Ratner, Yoav Y. Schechner
Venue: Conference CVPR 2007
Year: 2007

Taking a sequence of photographs using multiple illumination sources or settings is central to many computer vision and graphics problems. A growing number of recent methods use multiple sources rather than single point sources in each frame of the sequence. Potential benefits include increased signal-to-noise ratio and accommodation of scene dynamic range. However, existing multiplexing schemes, including Hadamard-based codes, are inhibited by fundamental limits set by Poisson distributed photon noise and by sensor saturation. The prior schemes may actually be counterproductive due to these effects. We derive multiplexing codes that are optimal under these fundamental effects. Thus, the novel codes generalize the prior schemes and have a much broader applicability. Our approach is based on formulating the problem as a constrained optimization. We further suggest an algorithm to solve this optimization problem. The superiority and effectiveness of the method is demonstrated in experiments involving object illumination.
In defense of nearest-neighbor based image classification
Authors: Oren Boiman, Eli Shechtman, Michal Irani
Venue: Conference CVPR 2008
Year: 2008

State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric nearest-neighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between these two families of approaches rendered NN-based image classifiers useless. We claim that the effectiveness of non-parametric NN-based image classification has been considerably undervalued. We argue that two practices commonly used in image classification methods, have led to the inferior performance of NN-based image classifiers: (i) Quantization of local image descriptors (used to generate "bags-of-words ", codebooks). (ii) Computation of 'image-to-image' distance, instead of 'image-to-class' distance. We propose a trivial NN-based classifier - NBNN, (Naive-Bayes nearest-neighbor), which employs NN- distances in the space of the local image descriptors (and not in the space of images). NBNN computes direct 'image- to-class' distances without descriptor quantization. We further show that under the Naive-Bayes assumption, the theoretically optimal image classifier can be accurately approximated by NBNN. Although NBNN is extremely simple, efficient, and requires no learning/training phase, its performance ranks among the top leading learning-based image classifiers. Empirical comparisons are shown on several challenging databases (Caltech-101 ,Caltech-256 and Graz-01).
Critical Event Prediction for Proactive Management in Large-scale Computer Clusters
Authors: Ramendra K. Sahoo, Adam J. Oliner, Irina Rish, Manish Gupta, José E. Moreira, Sheng Ma, Ricardo Vilalta, Anand Sivasubramaniam
Venue: Conference KDD 2003
Year: 2003

As the complexity of distributed computing systems increases, systems management tasks require significantly higher levels of automation; examples include diagnosis and prediction based on real-time streams of computer events, setting alarms, and performing continuous monitoring. The core of autonomic computing, a recently proposed initiative towards next-generation IT-systems capable of 'self-healing', is the ability to analyze data in real-time and to predict potential problems. The goal is to avoid catastrophic failures through prompt execution of remedial actions.This paper describes an attempt to build a proactive prediction and control system for large clusters. We collected event logs containing various system reliability, availability and serviceability (RAS) events, and system activity reports (SARs) from a 350-node cluster system for a period of one year. The 'raw' system health measurements contain a great deal of redundant event data, which is either repetitive in nature or misaligned with respect to time. We applied a filtering technique and modeled the data into a set of primary and derived variables. These variables used probabilistic networks for establishing event correlations through prediction algorithms. We also evaluated the role of time-series methods, rule-based classification algorithms and Bayesian network models in event prediction.Based on historical data, our results suggest that it is feasible to predict system performance parameters (SARs) with a high degree of accuracy using time-series models. Rule-based classification techniques can be used to extract machine-event signatures to predict critical events with up to 70% accuracy.
Action Recognition with Motion-appearance Vocabulary Forest
Authors: Krystian Mikolajczyk, Hirofumi Uemura
Venue: Conference CVPR 2008
Year: 2008

In this paper we propose an approach for action recognition based on a vocabulary forest of local motion-appearance features. Large numbers of features with associated motion vectors are extracted from action data and are represented by many vocabulary trees. Features from a query sequence are matched to the trees and vote for action categories and their locations. Large number of trees make the process efficient and robust. The system is capable of simultaneous categorization and localization of actions using only a few frames per sequence. The approach obtains excellent performance on standard action recognition sequences. We perform large scale experiments on 17 challenging real action categories from Olympic Games <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We demonstrate the robustness of our method to appearance variations, camera motion, scale change, asymmetric actions, background clutter and occlusion.
A Hough Transform-Based Voting Framework for Action Recognition
Authors: Angela Yao, Jürgen Gall, Luc Van Gool
Venue: Conference CVPR 2010
Year: 2010

We present a method to classify and localize human actions in video using a Hough transform voting framework. Random trees are trained to learn a mapping between densely-sampled feature patches and their corresponding votes in a spatio-temporal-action Hough space. The leaves of the trees form a discriminative multi-class codebook that share features between the action classes and vote for action centers in a probabilistic manner. Using low-level features such as gradients and optical flow, we demonstrate that Hough-voting can achieve state-of-the-art performance on several datasets covering a wide range of action-recognition scenarios.
Discriminative Figure-Centric Models for Joint Action Localization and Recognition
Authors: Tian Lan 0006, Yang Wang 0003, Greg Mori
Venue: Conference ICCV 2011
Year: 2011

In this paper we develop an algorithm for action recognition and localization in videos. The algorithm uses a figure-centric visual word representation. Different from previous approaches it does not require reliable human detection and tracking as input. Instead, the person location is treated as a latent variable that is inferred simultaneously with action recognition. A spatial model for an action is learned in a discriminative fashion under a figure-centric representation. Temporal smoothness over video sequences is also enforced. We present results on the UCF-Sports dataset, verifying the effectiveness of our model in situations where detection and tracking of individuals is challenging.
Studying from electronic textbooks
Authors: Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi
Venue: Conference CIKM 2013
Year: 2013

We present study navigator, an algorithmically-generated aid for enhancing the experience of studying from electronic textbooks. The study navigator for a section of the book consists of helpful concept references for understanding this section. Each concept reference is a pair consisting of a concept phrase explained elsewhere and the link to the section in which it has been explained. We propose a novel reader model for textbooks and an algorithm for generating the study navigator based on this model. We also present the results of an extensive user study that demonstrates the efficacy of the proposed system across textbooks on different subjects from different grades.
Discriminately decreasing discriminability with learned image filters
Authors: Jacob Whitehill, Javier R. Movellan
Venue: Conference CVPR 2012
Year: 2012

In machine learning and computer vision, input signals are often filtered to increase data discriminability. For example, preprocessing face images with Gabor band-pass filters is known to improve performance in expression recognition tasks [1]. Sometimes, however, one may wish to purposely decrease discriminability of one classification task (a “distractor” task), while simultaneously preserving information relevant to another task (the target task): For example, due to privacy concerns, it may be important to mask the identity of persons contained in face images before submitting them to a crowdsourcing site (e.g., Mechanical Turk) when labeling them for certain facial attributes. Suppressing discriminability in distractor tasks may also be needed to improve inter-dataset generalization: training datasets may sometimes contain spurious correlations between a target attribute (e.g., facial expression) and a distractor attribute (e.g., gender). We might improve generalization to new datasets by suppressing the signal related to the distractor task in the training dataset. This can be seen as a special form of supervised regularization. In this paper we present an approach to automatically learning preprocessing filters that suppress discriminability in distractor tasks while preserving it in target tasks. We present promising results in simulated image classification problems and in a realistic expression recognition problem.
Neural Attentive Session-based Recommendation
Authors: Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma
Venue: Conference CIKM 2017
Year: 2017

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.
Instagan: Instance-aware image-to-image translation
Authors: Sangwoo Mo, Minsu Cho, Jinwoo Shin
Venue: Conference ICLR 2019
Year: 2019

Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous methods often fail in challenging cases, in particular, when an image has multiple target instances and a translation task involves significant changes in shape, e.g., translating pants to skirts in fashion images. To tackle the issues, we propose a novel method, coined instance-aware GAN (InstaGAN), that incorporates the instance information (e.g., object segmentation masks) and improves multi-instance transfiguration. The proposed method translates both an image and the corresponding set of instance attributes while maintaining the permutation invariance property of the instances. To this end, we introduce a context preserving loss that encourages the network to learn the identity function outside of target instances. We also propose a sequential mini-batch inference/training technique that handles multiple instances with a limited GPU memory and enhances the network to generalize better for multiple instances. Our comparative evaluation demonstrates the effectiveness of the proposed method on different image datasets, in particular, in the aforementioned challenging cases. Code and results are available in https://github.com/sangwoomo/instagan
Content-based photo quality assessment
Authors: Wei Luo, Xiaogang Wang 0001, Xiaoou Tang
Venue: Conference ICCV 2011
Year: 2011

Automatically assessing photo quality from the perspective of visual aesthetics is of great interest in high-level vision research and has drawn much attention in recent years. In this paper, we propose content-based photo quality assessment using regional and global features. Under this framework, subject areas, which draw the most attentions of human eyes, are first extracted. Then regional features extracted from subject areas and the background regions are combined with global features to assess the photo quality. Since professional photographers may adopt different photographic techniques and may have different aesthetic criteria in mind when taking different types of photos (e.g. landscape versus portrait), we propose to segment regions and extract visual features in different ways according to the categorization of photo content. Therefore we divide the photos into seven categories based on their content and develop a set of new subject area extraction methods and new visual features, which are specially designed for different categories. This argument is supported by extensive experimental comparisons of existing photo quality assessment approaches as well as our new regional and global features over different categories of photos. Our new features significantly outperform the state-of-the-art methods. Another contribution of this work is to construct a large and diversified benchmark database for the research of photo quality assessment. It includes 17, 613 photos with manually labeled ground truth.
Detecting Music Genre using Extreme Gradient Boosting
Authors: Benjamin Murauer, Günther Specht
Venue: Conference WWW 2018
Year: 2018

This paper summarizes our contribution to the CrowdAI music genre classification challenge "Learning to Recognise Musical Genre from Audio on the Web'' as part of the WebConference 2018. We utilize different approaches from the field of music analysis to predict the music genre of given mp3 music files, including a convolutional neural network for spectrogram classification, deep neural networks and ensemble methods using various numerical audio features. Our best results were obtained by an extreme gradient boosting classifier.