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Institution

California Institute of Technology

EducationPasadena, California, United States
About: California Institute of Technology is a education organization based out in Pasadena, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 57649 authors who have published 146691 publications receiving 8620287 citations. The organization is also known as: Caltech & Cal Tech.


Papers
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Book ChapterDOI
TL;DR: This study addresses the question of how simple networks of neuron-like elements can account for a variety of phenomena associated with this shift of selective visual attention and suggests a possible role for the extensive back-projection from the visual cortex to the LGN.
Abstract: Psychophysical and physiological evidence indicates that the visual system of primates and humans has evolved a specialized processing focus moving across the visual scene. This study addresses the question of how simple networks of neuron-like elements can account for a variety of phenomena associated with this shift of selective visual attention. Specifically, we propose the following: (1) A number of elementary features, such as color, orientation, direction of movement, disparity etc. are represented in parallel in different topographical maps, called the early representation. (2) There exists a selective mapping from the early topographic representation into a more central non-topographic representation, such that at any instant the central representation contains the properties of only a single location in the visual scene, the selected location. We suggest that this mapping is the principal expression of early selective visual attention. One function of selective attention is to fuse information from different maps into one coherent whole. (3) Certain selection rules determine which locations will be mapped into the central representation. The major rule, using the conspicuity of locations in the early representation, is implemented using a so-called Winner-Take-All network. Inhibiting the selected location in this network causes an automatic shift towards the next most conspicious location. Additional rules are proximity and similarity preferences. We discuss how these rules can be implemented in neuron-like networks and suggest a possible role for the extensive back-projection from the visual cortex to the LGN.

3,930 citations

Proceedings ArticleDOI
20 Jun 2005
TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.
Abstract: We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.

3,920 citations

01 Jul 2011
TL;DR: CUB-200-2011 as mentioned in this paper is an extended version of CUB200, which roughly doubles the number of images per category and adds new part localization annotations, annotated with bounding boxes, part locations, and at-ribute labels.
Abstract: CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The extended version roughly doubles the number of images per category and adds new part localization annotations. All images are annotated with bounding boxes, part locations, and at- tribute labels. Images and annotations were filtered by mul- tiple users of Mechanical Turk. We introduce benchmarks and baseline experiments for multi-class categorization and part localization.

3,769 citations

Journal ArticleDOI
20 Aug 2004
TL;DR: The Swift mission as discussed by the authors is a multi-wavelength observatory for gamma-ray burst (GRB) astronomy, which is a first-of-its-kind autonomous rapid-slewing satellite for transient astronomy and pioneers the way for future rapid-reaction and multiwavelength missions.
Abstract: The Swift mission, scheduled for launch in 2004, is a multiwavelength observatory for gamma-ray burst (GRB) astronomy. It is a first-of-its-kind autonomous rapid-slewing satellite for transient astronomy and pioneers the way for future rapid-reaction and multiwavelength missions. It will be far more powerful than any previous GRB mission, observing more than 100 bursts yr � 1 and performing detailed X-ray and UV/optical afterglow observations spanning timescales from 1 minute to several days after the burst. The objectives are to (1) determine the origin of GRBs, (2) classify GRBs and search for new types, (3) study the interaction of the ultrarelativistic outflows of GRBs with their surrounding medium, and (4) use GRBs to study the early universe out to z >10. The mission is being developed by a NASA-led international collaboration. It will carry three instruments: a newgeneration wide-field gamma-ray (15‐150 keV) detector that will detect bursts, calculate 1 0 ‐4 0 positions, and trigger autonomous spacecraft slews; a narrow-field X-ray telescope that will give 5 00 positions and perform spectroscopy in the 0.2‐10 keV band; and a narrow-field UV/optical telescope that will operate in the 170‐ 600 nm band and provide 0B3 positions and optical finding charts. Redshift determinations will be made for most bursts. In addition to the primary GRB science, the mission will perform a hard X-ray survey to a sensitivity of � 1m crab (� 2;10 � 11 ergs cm � 2 s � 1 in the 15‐150 keV band), more than an order of magnitude better than HEAO 1 A-4. A flexible data and operations system will allow rapid follow-up observations of all types of

3,753 citations


Authors

Showing all 58155 results

NameH-indexPapersCitations
Eric S. Lander301826525976
Donald P. Schneider2421622263641
George M. Whitesides2401739269833
Yi Chen2174342293080
David Baltimore203876162955
Edward Witten202602204199
George Efstathiou187637156228
Michael A. Strauss1851688208506
Jing Wang1844046202769
Ruedi Aebersold182879141881
Douglas Scott1781111185229
Hyun-Chul Kim1764076183227
Phillip A. Sharp172614117126
Timothy M. Heckman170754141237
Zhenan Bao169865106571
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023176
2022737
20214,682
20205,519
20195,321
20185,133