2021 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to scene recognition, large-scale visual datasets, and transfer learning.
2010 - IAPR J. K. Aggarwal Prize "For algorithms and representations to exploit context in computer vision."
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Object, Cognitive neuroscience of visual object recognition and Pattern recognition. His Artificial intelligence research incorporates themes from Context and Machine learning. His Computer vision research includes elements of Scene statistics, Visual processing, Categorization and Pattern recognition.
His study in Object is interdisciplinary in nature, drawing from both Image processing, Feature extraction, Parsing and Bundle adjustment. Antonio Torralba combines subjects such as Communication, Image retrieval, Database and Human visual system model with his study of Cognitive neuroscience of visual object recognition. Antonio Torralba has included themes like Artificial neural network, Focus, Representation and Contextual image classification in his Pattern recognition study.
Antonio Torralba spends much of his time researching Artificial intelligence, Computer vision, Object, Pattern recognition and Machine learning. His study in Image, Object detection, Cognitive neuroscience of visual object recognition, Artificial neural network and Convolutional neural network falls within the category of Artificial intelligence. His research integrates issues of Context, Visualization and Scene statistics in his study of Computer vision.
His Object research is multidisciplinary, incorporating perspectives in Human–computer interaction and Pattern recognition. His Pattern recognition study combines topics from a wide range of disciplines, such as Viola–Jones object detection framework and Feature. His research in Machine learning intersects with topics in Representation and Inference.
Antonio Torralba mainly investigates Artificial intelligence, Computer vision, Artificial neural network, Object and Generative model. His Artificial intelligence study combines topics in areas such as Natural language processing, Machine learning and Pattern recognition. While the research belongs to areas of Computer vision, Antonio Torralba spends his time largely on the problem of Leverage, intersecting his research to questions surrounding Inverse problem and Inverse kinematics.
As a member of one scientific family, Antonio Torralba mostly works in the field of Artificial neural network, focusing on Deep learning and, on occasion, Optical flow, Speech recognition and Pose. The concepts of his Object study are interwoven with issues in Pixel, Task, Human–computer interaction and Image editing. His studies deal with areas such as Ground truth and Feature extraction as well as Visualization.
Antonio Torralba focuses on Artificial intelligence, Object, Artificial neural network, Computer vision and Visualization. Antonio Torralba interconnects Machine learning, Pattern recognition and Natural language processing in the investigation of issues within Artificial intelligence. His work deals with themes such as Pixel, Task and Image editing, which intersect with Object.
His Artificial neural network research includes themes of Generative model and Graph. His research is interdisciplinary, bridging the disciplines of Polygon mesh and Computer vision. His Visualization research integrates issues from Motion, Image segmentation, Object detection, Ground truth and Feature extraction.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva;Antonio Torralba.
International Journal of Computer Vision (2001)
LabelMe: A Database and Web-Based Tool for Image Annotation
Bryan C. Russell;Antonio Torralba;Kevin P. Murphy;William T. Freeman.
International Journal of Computer Vision (2008)
Learning Deep Features for Discriminative Localization
Bolei Zhou;Aditya Khosla;Agata Lapedriza;Aude Oliva.
computer vision and pattern recognition (2016)
Learning Deep Features for Scene Recognition using Places Database
Bolei Zhou;Agata Lapedriza;Jianxiong Xiao;Antonio Torralba.
neural information processing systems (2014)
Spectral Hashing
Yair Weiss;Antonio Torralba;Rob Fergus.
neural information processing systems (2008)
SUN database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao;James Hays;Krista A. Ehinger;Aude Oliva.
computer vision and pattern recognition (2010)
Learning to predict where humans look
Tilke Judd;Krista Ehinger;Fredo Durand;Antonio Torralba.
international conference on computer vision (2009)
Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search.
Antonio Torralba;Aude Oliva;Monica S. Castelhano;John M. Henderson.
Psychological Review (2006)
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
A. Torralba;R. Fergus;W.T. Freeman.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)
Building the gist of a scene: the role of global image features in recognition.
Aude Oliva;Antonio Torralba.
Progress in Brain Research (2006)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
MIT
Columbia University
Chinese University of Hong Kong
MIT
Stanford University
Carnegie Mellon University
University of Toronto
MIT
Ben-Gurion University of the Negev
Microsoft (United States)
University of Pisa
University of Nantes
Osaka University
Nankai University
Jilin University
Utrecht University
Université Libre de Bruxelles
Yale University
Johns Hopkins University
University of Minnesota
Eli Lilly (United States)
Charité - University Medicine Berlin
Weizmann Institute of Science
Princeton University