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59. Low-Power Computer Vision: Status, Challenges, and Opportunities

Author

Alyamkin, Sergei, primary, Ardi, Matthew, additional, Berg, Alexander C., additional, Brighton, Achille, additional, Chen, Bo, additional, Chen, Yiran, additional, Cheng, Hsin-Pai, additional, Fan, Zichen, additional, Feng, Chen, additional, Fu, Bo, additional, Gauen, Kent, additional, Goel, Abhinav, additional, Goncharenko, Alexander, additional, Guo, Xuyang, additional, Ha, Soonhoi, additional, Howard, Andrew, additional, Hu, Xiao, additional, Huang, Yuanjun, additional, Kang, Donghyun, additional, Kim, Jaeyoun, additional, Ko, Jong Gook, additional, Kondratyev, Alexander, additional, Lee, Junhyeok, additional, Lee, Seungjae, additional, Lee, Suwoong, additional, Li, Zichao, additional, Liang, Zhiyu, additional, Liu, Juzheng, additional, Liu, Xin, additional, Lu, Yang, additional, Lu, Yung-Hsiang, additional, Malik, Deeptanshu, additional, Nguyen, Hong Hanh, additional, Park, Eunbyung, additional, Repin, Denis, additional, Shen, Liang, additional, Sheng, Tao, additional, Sun, Fei, additional, Svitov, David, additional, Thiruvathukal, George K., additional, Zhang, Baiwu, additional, Zhang, Jingchi, additional, Zhang, Xiaopeng, additional, and Zhuo, Shaojie, additional

Published
2019
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61. Low-Power Image Recognition Challenge

Author

Lu, Yung-Hsiang, Berg, Alexander C., and Chen, Yiran

Subjects
Cameras (Photography) -- Innovations -- Energy use, Energy consumption, Business
Abstract

The Low-Power Image Recognition Challenge (LPIRC) has been held annually since 2015. This article summarizes the competition advancements made over the past three years. ********** Cameras have become available in [...]

Published
2018

62. Learning to Name Objects.

Author

Ordonez, Vicente, Wei Liu, Jia Deng, Yejin Choi, Berg, Alexander C., and Berg, Tamara L.

Subjects
NAMES, NATURAL language processing, COMPUTER vision, PSYCHOLOGISTS, COMPUTER science
Abstract

We have seen remarkable recent progress in computational visual recognition, producing systems that can classify objects into thousands of different categories with increasing accuracy. However, one question that has received relatively less attention is “what labels should recognition systems output?” This paper looks at the problem of predicting category labels that mimic how human observers would name objects. This goal is related to the concept of entry-level categories first introduced by psychologists in the 1970s and 1980s. We extend these seminal ideas to study human naming at large scale and to learn computational models for predicting entry-level categories. Practical applications of this work include improving human-focused computer vision applications such as automatically generating a natural language description for an image or text-based image search. [ABSTRACT FROM AUTHOR]

Published
2016
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74. Rebooting Computing and Low-Power Image Recognition Challenge

Author

Lu, Yung-Hsiang, primary, Kadin, Alan M., additional, Berg, Alexander C., additional, Conte, Thomas M., additional, DeBenedictis, Erik P., additional, Garg, Rachit, additional, Gingade, Ganesh, additional, Hoang, Bichlien, additional, Huang, Yongzhen, additional, Li, Boxun, additional, Liu, Jingyu, additional, Liu, Wei, additional, Mao, Huizi, additional, Peng, Junran, additional, Tang, Tianqi, additional, Track, Elie K., additional, Wang, Jingqiu, additional, Wang, Tao, additional, Wang, Yu, additional, and Yao, Jun, additional

Published
2015
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75. Large Scale Retrieval and Generation of Image Descriptions

Author

Ordonez, Vicente, primary, Han, Xufeng, additional, Kuznetsova, Polina, additional, Kulkarni, Girish, additional, Mitchell, Margaret, additional, Yamaguchi, Kota, additional, Stratos, Karl, additional, Goyal, Amit, additional, Dodge, Jesse, additional, Mensch, Alyssa, additional, Daumé, Hal, additional, Berg, Alexander C., additional, Choi, Yejin, additional, and Berg, Tamara L., additional

Published
2015
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93. Understanding and predicting importance in images.

Author

Berg, Alexander C., Berg, Tamara L., Daume, Hal, Dodge, Jesse, Goyal, Amit, Han, Xufeng, Mensch, Alyssa, Mitchell, Margaret, Sood, Aneesh, Stratos, Karl, and Yamaguchi, Kota

Abstract

What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1) factors related to composition, e.g. size, location, 2) factors related to semantics, e.g. category of object or scene, and 3) contextual factors related to the likelihood of attribute-object, or object-scene pairs. We explore these factors using what people describe as a proxy for importance. Finally, we build models to predict what will be described about an image given either known image content, or image content estimated automatically by recognition systems. [ABSTRACT FROM PUBLISHER]

Published
2012
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94. DCMSVM: Distributed parallel training for single-machine multiclass classifiers.

Author

Han, Xufeng and Berg, Alexander C.

Abstract

We present an algorithm and implementation for distributed parallel training of single-machine multiclass SVMs. While there is ongoing and healthy debate about the best strategy for multiclass classification, there are some features of the single-machine approach that are not available when training alternatives such as one-vs-all, and that are quite complex for tree based methods. One obstacle to exploring single-machine approaches on large datasets is that they are usually limited to running on a single machine! We build on a framework borrowed from parallel convex optimization — the alternating direction method of multipliers (ADMM) — to develop a new consensus based algorithm for distributed training of single-machine approaches. This is demonstrated with an implementation of our novel sequential dual algorithm (DCMSVM) which allows distributed parallel training with small communication requirements. Benchmark results show significant reduction in wall clock time compared to current state of the art multiclass SVM implementation (Liblinear) on a single node. Experiments are performed on large scale image classification including results with modern high-dimensional features. [ABSTRACT FROM PUBLISHER]

Published
2012
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95. Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition.

Author

Deng, Jia, Krause, Jonathan, Berg, Alexander C., and Fei-Fei, Li

Abstract

As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracy-specificity trade-offs in large scale recognition, motivated by the observation that object categories form a semantic hierarchy consisting of many levels of abstraction. A classifier can select the appropriate level, trading off specificity for accuracy in case of uncertainty. By optimizing this trade-off, we obtain classifiers that try to be as specific as possible while guaranteeing an arbitrarily high accuracy. We formulate the problem as maximizing information gain while ensuring a fixed, arbitrarily small error rate with a semantic hierarchy. We propose the Dual Accuracy Reward Trade-off Search (DARTS) algorithm and prove that, under practical conditions, it converges to an optimal solution. Experiments demonstrate the effectiveness of our algorithm on datasets ranging from 65 to over 10,000 categories. [ABSTRACT FROM PUBLISHER]

Published
2012
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96. Shape Matching and Object Recognition.

Author

Ponce, Jean, Hebert, Martial, Schmid, Cordelia, Zisserman, Andrew, Berg, Alexander C., and Malik, Jitendra

Abstract

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Li, Fergus and Perona), a challenging dataset with large intraclass variation. Our approach yields a 45% correct classification rate in addition to localization. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces. [ABSTRACT FROM AUTHOR]

Published
2006
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97. Low-Power Image Recognition Challenge.

Author

Yung-Hsiang Lu, Berg, Alexander C., and Yiran Chen

Subjects
IMAGE recognition (Computer vision), ECONOMIC competition, TECHNOLOGICAL innovations, ENERGY consumption, HTTP (Computer network protocol), FIELD programmable gate arrays, ARTIFICIAL neural networks
Abstract

The Low-Power Image Recognition Challenge (LPIRC) has been held annually since 2015. This article summarizes the competition advancements made over the past three years. [ABSTRACT FROM AUTHOR]

Published
2018
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98. Modeling guidance and recognition in categorical search: bridging human and computer object detection.

Author

Zelinsky GJ, Peng Y, Berg AC, and Samaras D

Subjects
Eye Movements physiology, Humans, Reaction Time, Image Processing, Computer-Assisted, Pattern Recognition, Visual physiology
Abstract

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery.

Published
2013
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99. Describable Visual Attributes for Face Verification and Image Search.

Author

Kumar N, Berg AC, Belhumeur PN, and Nayar SK

Subjects
Algorithms, Female, Humans, Male, Biometric Identification methods, Face anatomy & histology, Image Processing, Computer-Assisted methods
Abstract

We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: They can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images. We demonstrate the current effectiveness--and explore the future potential--of using attributes for face verification and image search via human and computational experiments. Finally, we introduce two new face data sets, named FaceTracer and PubFig, with labeled attributes and identities, respectively.

Published
2011
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