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
More filters
Proceedings ArticleDOI
Simultaneous Deep Transfer Across Domains and Tasks
TL;DR: This work proposes a new CNN architecture to exploit unlabeled and sparsely labeled target domain data and simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks.
Proceedings ArticleDOI
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Fisher Yu,Haofeng Chen,Xin Wang,Wenqi Xian,Yingying Chen,Fangchen Liu,Vashisht Madhavan,Trevor Darrell +7 more
TL;DR: This work constructs BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving and shows that special training strategies are needed for existing models to perform such heterogeneous tasks.
Book ChapterDOI
Part-Based R-CNNs for Fine-Grained Category Detection
TL;DR: In this article, the authors propose a model for fine-grained categorization by leveraging deep convolutional features computed on bottom-up region proposals, which learns whole-object and part detectors, enforces learned geometric constraints between them, and predicts a finegrained category from a pose normalized representation.
Proceedings ArticleDOI
Neural Module Networks
TL;DR: The authors decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained.
Proceedings ArticleDOI
Deep Layer Aggregation
TL;DR: Deep layer aggregation as mentioned in this paper iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters, and experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes.