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Trevor Darrell

32 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author15
  • last author17

Across the 32 of 32 papers where every author was matched, so the position is known.

fields
  • cs.CV25
  • cs.LG5
  • cs.CL1
  • cs.MM1
ORCID 0000-0001-5453-8533
same name
  • Trevor Darrell — 120 papers, h 158
  • Trevor Darrell — 22 papers, h 11
  • Trevor Darrell — 21 papers, h 12
  • Trevor Darrell — 17 papers, h 10
  • Trevor Darrell — 13 papers, h 12
  • Trevor Darrell — 11 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20122025
most citedCaffe: Convolutional Architecture for Fast Feature Embedding

4.3k citations · 12k across the 32 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022★ 50 cited

Visual Prompting via Image Inpainting

Amir Bar, Yossi Gandelsman, Trevor Darrell +2

How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investi…

cs.CV2022

TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency

Medhini Narasimhan, Arsha Nagrani, Chen Sun +4

YouTube users looking for instructions for a specific task may spend a long time browsing content trying to find the right video that matches their needs. Creating a visual summary…

cs.CV2022

Disentangled Action Recognition with Knowledge Bases

Zhekun Luo, Shalini Ghosh, Devin Guillory +3

Action in video usually involves the interaction of human with objects. Action labels are typically composed of various combinations of verbs and nouns, but we may not have trainin…

cs.LG2022★ 3 cited

Explaining Reinforcement Learning Policies through Counterfactual Trajectories

Julius Frost, Olivia Watkins, Eric Weiner +4

In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time. Some policy…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.