T
Trevor Darrell
Researcher at University of California, Berkeley
Publications - 734
Citations - 222973
Trevor Darrell is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 148, co-authored 678 publications receiving 181113 citations. Previous affiliations of Trevor Darrell include Massachusetts Institute of Technology & Boston University.
Papers
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Journal ArticleDOI
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue,Lisa Anne Hendricks,Marcus Rohrbach,Subhashini Venugopalan,Sergio Guadarrama,Kate Saenko,Trevor Darrell +6 more
TL;DR: In this article, a class of recurrent convolutional architectures was proposed for large-scale visual understanding tasks, and demonstrated the value of these models for activity recognition, image captioning, and video description.
Proceedings ArticleDOI
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
TL;DR: This paper introduces ARC-t, a flexible model for supervised learning of non-linear transformations between domains, based on a novel theoretical result demonstrating that such transformations can be learned in kernel space.
Proceedings ArticleDOI
Curiosity-Driven Exploration by Self-Supervised Prediction
TL;DR: In this article, the authors formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model.
Posted Content
BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling.
TL;DR: The design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets, and a new driving dataset, which is an order of magnitude larger than previous efforts.
Posted Content
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
TL;DR: This paper introduces the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems, and outperforms baselines across different settings on multiple large-scale datasets.