Search Results for author: Benjamin Sapp

Found 10 papers, 4 papers with code

Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios

no code implementations21 Dec 2022 Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Rebecca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine

To our knowledge, this is the first application of a combined imitation and reinforcement learning approach in autonomous driving that utilizes large amounts of real-world human driving data.

Autonomous Driving Imitation Learning +2

JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving

no code implementations16 Dec 2022 Wenjie Luo, Cheolho Park, Andre Cornman, Benjamin Sapp, Dragomir Anguelov

We propose JFP, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories.

Autonomous Driving Future prediction

Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting

no code implementations8 Jun 2022 DiJia Su, Bertrand Douillard, Rami Al-Rfou, Cheolho Park, Benjamin Sapp

These models are intrinsically invariant to translation and rotation between scene elements, are best-performing on public leaderboards, but scale quadratically with the number of agents and scene elements.

Knowledge Distillation Motion Forecasting +2

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

1 code implementation20 Nov 2015 Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei

Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.

Ranked #4 on Fine-Grained Image Classification on CUB-200-2011 (using extra training data)

Active Learning Fine-Grained Image Classification

Sidestepping Intractable Inference with Structured Ensemble Cascades

no code implementations NeurIPS 2010 David Weiss, Benjamin Sapp, Ben Taskar

For many structured prediction problems, complex models often require adopting approximate inference techniques such as variational methods or sampling, which generally provide no satisfactory accuracy guarantees.

Pose Estimation Structured Prediction

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