The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

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ImageNet Large Scale Visual Recognition Challenge

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Multiple View Geometry in Computer Vision.

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

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Object tracking: A survey

There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-training the network at different resolutions. However retraining a network is rarely cheap and not always viable. To this end, we propose a simple solution to address the train-test distributional shift and enhance the performance of pretrained models - which commonly ship as a package with deep learning platforms e.g., PyTorch. Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off. Instead we propose to combine results for multiple random crops for a test image. This not only matches the train time augmentation but also provides the full coverage of the input image. We explore combining representation of random crops through averaging at different levels i.e., deep feature level, logit level, and softmax level. We demonstrate that, for various families of modern deep networks, such averaging results in better validation accuracy compared to using a single central crop per image. The softmax averaging results in the best performance for various pre-trained networks without requiring any re-training or fine-tuning whatsoever. On modern GPUs with batch processing, the paper's approach to inference of pre-trained networks, is essentially free as all images in a batch can all be processed at once. Our code is available at: https://github.com/TouqeerAhmad/MID

Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions

Next generation machine learning requires stepping away from classical batch learning towards interactive and adaptive learning. This is essential to cope with demanding machine learning applications we have already today. Our workshop at ECCV 2018 in Munich therefore served as a discussion forum for experts in this field and in the following we give a brief overview. Please note that this discussion paper has not been not peer-reviewed and only contains the subjective summary of the workshop organizers.

https://openaccess.thecvf.com/content_ECCVW_2018/papers/11134/Freytag_Workshop_on_Interactive_and_Adaptive_Learning_in_an_Open_World_ECCVW_2018_paper.pdf

Workshop on Interactive and Adaptive Learning in an Open World

This is an overview of image understanding research at Columbia’s Center for Research in Intelligent Systems since the 1993 IU workshop. It reviews our work on the following topics: 1. Physical Models and Sensors (a) Seeing Beyond Lambert’s Law (b) Impossible Shape from Shading (c) Specular Reflection and Stereo Vision (d) Color and Polarization (e) Active Shape from Focus 2. Visual Learning and Recognition (a) Learning Object Appearance Models (b) Real-Time Object Recognition System (c) Software Library for Appearance Matching 3. Sensor and Illumination Planning (a) Illumination Planning for Recognition (b) Model Based Sensor Planning

Image Understanding Research at Columbia University

Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this chapter, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A machine learning approach and several correlation filter approaches, including our own adaptive variant, are compared. We present experimental results for a variety of controlled data sets (derived from FERET and CMU PIE) that have been re-imaged under the difficult conditions of interest with an EMCCD based acquisition system, as well as on a realistic surveillance oriented set (SCface). The results of our experiments show that our detection approaches are extremely accurate under all tested conditions, and significantly improve detection accuracy compared to a leading commercial detector. This unique evaluation brings us one step closer to a better solution for the unconstrained face recognition problem.

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A Look at Eye Detection for Unconstrained Environments

http://cs.uccs.edu/~gsc/pub/master/jlogan/doc/James_Logan_CS701_MasterProject_Presentation.pdf

Terrance E. Boult

Papers

Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions

Workshop on Interactive and Adaptive Learning in an Open World

Image Understanding Research at Columbia University

A Look at Eye Detection for Unconstrained Environments

Behavior Model for Predictive Tracking of Multiple Targets