Trevor Darrell

Few-Shot Segmentation Propagation with Guided Networks

2018 · arXiv (Cornell University)

Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for …

Teachable Reinforcement Learning via Advice Distillation

2022 · arXiv (Cornell University)

Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate …

Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence

2023 · arXiv (Cornell University)

Diffusion models have been shown to be capable of generating high-quality images, suggesting that they could contain meaningful internal representations. Unfortunately, the feature maps that encode a diffusion model's internal information are spread not only …

Are Large Reasoning Models Interruptible?

2025 · arXiv (Cornell University)

Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments. In this work, we challenge the frozen world assumption and evaluate LRM robustness under two realistic dynamic scenarios: interruptions, which …

Learning to Compose Neural Networks for Question Answering

2016

Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.