Technical Reports - Trevor Darrell
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models (EECS-2024-206)
Long Lian, Boyi Li, Adam Yala and Trevor Darrell
Efficient and Scalable Large Multimodal Models (EECS-2024-186)
Sheng Shen
Motion Diffusion From Speech (EECS-2024-113)
Kushal Khangaonkar, Sanjay Subramanian, Daniel Klein and Trevor Darrell
Modeling Social Interactions from Multimodal Signals (EECS-2024-101)
Evonne Ng
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning (EECS-2023-263)
Ritwik Gupta, Colorado Reed, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele and Trevor Darrell
Learning to Generalize in Dynamic Environments (EECS-2022-229)
Dequan Wang
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly (EECS-2022-137)
Vedaad Shakib, Spencer Whitehead and Suzanne Petryk
Knowledge-Guided Self-Supervised Vision Transformers for Medical Imaging (EECS-2022-56)
Kevin Miao, Colorado Reed, Akash Gokul, Suzanne Petryk, Raghav Singh, Kurt Keutzer, Joseph Gonzalez and Trevor Darrell
AI for HADR: Progress and Opportunities (EECS-2020-233)
Ross Luo, Michael Laielli, Giscard Biamby and Adam Loeffler
Program Synthesis for Autonomous Driving Decisions (EECS-2020-114)
Yiteng Zhang, Yang Gao, Li Erran Li, Xinyun Chen and Trevor Darrell
End to End Learning in Autonomous Driving Systems (EECS-2020-5)
Yang Gao and Trevor Darrell
LabelAR: A spatial guidance interface for fast computer vision image collection (EECS-2019-58)
James Smith, Michael Laielli, Giscard Biamby, Trevor Darrell and Björn Hartmann
The BDD-Nexar Collective: A Large-Scale, Crowsourced, Dataset of Driving Scenes (EECS-2017-113)
Vashisht Madhavan and Trevor Darrell
Long-term Recurrent Convolutional Networks for Visual Recognition and Description (EECS-2014-180)
Jeffrey Donahue, Lisa Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko and Trevor Darrell
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations (EECS-2013-154)
Erik Rodner, Judith Hoffman, Jeffrey Donahue, Trevor Darrell and Kate Saenko
Visual Grasp Affordances From Appearance-Based Cues (EECS-2013-16)
Hyun Oh Song, Mario Fritz, Chunhui Gu and Trevor Darrell
Mid-level Features Improve Recognition of Interactive Activities (EECS-2012-209)
Kate Saenko, Ben Packer, C.-Y. Chen, S. Bandla, Y. Lee, Yangqing Jia, J.-C. Niebles, D. Koller, L. Fei-Fei, K. Grauman and Trevor Darrell
Visually-Grounded Bayesian Word Learning (EECS-2012-202)
Yangqing Jia, Joshua Abbott, Joseph Austerweil, Thomas Griffiths and Trevor Darrell
The Berkeley 3D Object Dataset (EECS-2012-85)
Allison Janoch
Don't Look Back: Post-hoc Category Detection via Sparse Reconstruction (EECS-2012-16)
Hyun Oh Song, Mario Fritz, Tim Althoff and Trevor Darrell
The Ratio Method for Multi-view Color Constancy (EECS-2011-23)
Trevor Owens, Kate Saenko, Ayan Chakrabarti, Ying Xiong, Todd Zickler and Trevor Darrell
Visual Domain Adaptation Using Regularized Cross-Domain Transforms (EECS-2010-106)
Kate Saenko, Brian Kulis, Mario Fritz and Trevor Darrell
Visual Domain Adaptation Using Regularized Cross-Domain Transforms (EECS-2010-105)
Kate Saenko, Brian Kulis, Mario Fritz and Trevor Darrell
Factorized Latent Spaces with Structured Sparsity (EECS-2010-99)
Yangqing Jia, Mathieu Salzmann and Trevor Darrell
Transferring Visual Category Models to New Domains (EECS-2010-54)
Kate Saenko, Brian Kulis, Mario Fritz and Trevor Darrell
Learning to Hash with Binary Reconstructive Embeddings (EECS-2009-101)
Brian Kulis and Trevor Darrell
Bayesian Localized Multiple Kernel Learning (EECS-2009-96)
Mario Christoudias, Raquel Urtasun and Trevor Darrell
Co-training with Noisy Perceptual Observations (EECS-2009-17)
C. Mario Christhoudias, Raquel Urtasun, Ashish Kapoor and Trevor Darrell
Probabilistic Kernel Combination for Hierarchical Object Categorization (EECS-2009-16)
Ashish Kapoor, Raquel Urtasun and Trevor Darrell