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Tamara L. Berg
Researcher at University of North Carolina at Chapel Hill
Publications - 110
Citations - 22326
Tamara L. Berg is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Question answering & Natural language. The author has an hindex of 48, co-authored 106 publications receiving 18212 citations. Previous affiliations of Tamara L. Berg include University of California, Berkeley & State University of New York System.
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Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
Proceedings Article
Im2Text: Describing Images Using 1 Million Captioned Photographs
TL;DR: A new objective performance measure for image captioning is introduced and methods incorporating many state of the art, but fairly noisy, estimates of image content are developed to produce even more pleasing results.
Proceedings ArticleDOI
Shape matching and object recognition using low distortion correspondences
TL;DR: This work approaches recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points, and shows results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.
Proceedings ArticleDOI
ReferItGame: Referring to Objects in Photographs of Natural Scenes
TL;DR: A new game to crowd-source natural language referring expressions by designing a two player game that can both collect and verify referring expressions directly within the game and provides an in depth analysis of the resulting dataset.
Journal ArticleDOI
BabyTalk: Understanding and Generating Simple Image Descriptions
Girish Kulkarni,Visruth Premraj,Vicente Ordonez,Sagnik Dhar,Siming Li,Yejin Choi,Alexander C. Berg,Tamara L. Berg +7 more
TL;DR: The proposed system to automatically generate natural language descriptions from images is very effective at producing relevant sentences for images and generates descriptions that are notably more true to the specific image content than previous work.