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Displaying 1-11 out of 11 total
Modeling Radiometric Uncertainty for Vision with Tone-Mapped Color Images
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ayan Chakrabarti,Ying Xiong,Baochen Sun,Trevor Darrell,Daniel Scharstein,Todd Zickler,Kate Saenko
Issue Date:November 2014
pp. 1-1
To produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range. This introduces non-linear distortion that must be undone, through a radiometric...
 
Confidence-Rated Multiple Instance Boosting for Object Detection
Found in: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Karim Ali,Kate Saenko
Issue Date:June 2014
pp. 2433-2440
Over the past years, Multiple Instance Learning (MIL) has proven to be an effective framework for learning with weakly labeled data. Applications of MIL to object detection, however, were limited to handling the uncertainties of manual annotations. In this...
 
Continuous Manifold Based Adaptation for Evolving Visual Domains
Found in: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Judy Hoffman,Trevor Darrell,Kate Saenko
Issue Date:June 2014
pp. 867-874
We pose the following question: what happens when test data not only differs from training data, but differs from it in a continually evolving way? The classic domain adaptation paradigm considers the world to be separated into stationary domains with clea...
 
YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Sergio Guadarrama,Niveda Krishnamoorthy,Girish Malkarnenkar,Subhashini Venugopalan,Raymond Mooney,Trevor Darrell,Kate Saenko
Issue Date:December 2013
pp. 2712-2719
Despite a recent push towards large-scale object recognition, activity recognition remains limited to narrow domains and small vocabularies of actions. In this paper, we tackle the challenge of recognizing and describing activities ``in-the-wild''. We pres...
 
Multistream Articulatory Feature-Based Models for Visual Speech Recognition
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Kate Saenko, Karen Livescu, James Glass, Trevor Darrell
Issue Date:September 2009
pp. 1700-1707
We study the problem of automatic visual speech recognition (VSR) using dynamic Bayesian network (DBN)-based models consisting of multiple sequences of hidden states, each corresponding to an articulatory feature (AF) such as lip opening (LO) or lip roundi...
 
Visual Speech Recognition with Loosely Synchronized Feature Streams
Found in: Computer Vision, IEEE International Conference on
By Kate Saenko, Karen Livescu, Michael Siracusa, Kevin Wilson,, James Glass, Trevor Darrell
Issue Date:October 2005
pp. 1424-1431
We present an approach to detecting and recognizing spoken isolated phrases based solely on visual input. We adopt an architecture that first employs discriminative detection of visual speech and articulatory features, and then performs recognition using a...
 
Semi-supervised Domain Adaptation with Instance Constraints
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Jeff Donahue,Judy Hoffman,Erik Rodner,Kate Saenko,Trevor Darrell
Issue Date:June 2013
pp. 668-675
Most successful object classification and detection methods rely on classifiers trained on large labeled datasets. However, for domains where labels are limited, simply borrowing labeled data from existing datasets can hurt performance, a phenomenon known ...
 
Towards adaptive object recognition for situated human-computer interaction
Found in: Proceedings of the 2007 workshop on Multimodal interfaces in semantic interaction (WMISI '07)
By Kate Saenko
Issue Date:November 2007
pp. 43-46
Object recognition is an important part of human-computer interaction in situated environments, such as a home or an office. Especially useful is category-level recognition (e.g., recognizing the class of chairs, as opposed to a particular chair.) While hu...
     
Co-Adaptation of audio-visual speech and gesture classifiers
Found in: Proceedings of the 8th international conference on Multimodal interfaces (ICMI '06)
By C. Mario Christoudias, Kate Saenko, Louis-Philippe Morency, Trevor Darrell
Issue Date:November 2006
pp. 84-91
The construction of robust multimodal interfaces often requires large amounts of labeled training data to account for cross-user differences and variation in the environment. In this work, we investigate whether unlabeled training data can be leveraged to ...
     
A segment-based audio-visual speech recognizer: data collection, development, and initial experiments
Found in: Proceedings of the 6th international conference on Multimodal interfaces (ICMI '04)
By Chia-Hao La, James R. Glass, Kate Saenko, Timothy J. Hazen
Issue Date:October 2004
pp. 235-242
This paper presents the development and evaluation of a speaker-independent audio-visual speech recognition (AVSR) system that utilizes a segment-based modeling strategy. To support this research, we have collected a new video corpus, called Audio-Visual T...
     
Articulatory features for robust visual speech recognition
Found in: Proceedings of the 6th international conference on Multimodal interfaces (ICMI '04)
By James R. Glass, Kate Saenko, Trevor Darrell
Issue Date:October 2004
pp. 152-158
Visual information has been shown to improve the performance of speech recognition systems in noisy acoustic environments. However, most audio-visual speech recognizers rely on a clean visual signal. In this paper, we explore a novel approach to visual spe...
     
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