activity
20162024
most citedN2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning

116 citations · 214 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting

Jiezhi Yang, Khushi Desai, Charles Packer +4

We propose CARFF, a method for predicting future 3D scenes given past observations. Our method maps 2D ego-centric images to a distribution over plausible 3D latent scene configura…

cs.CV2020

Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting

Xinshuo Weng, Jianren Wang, Sergey Levine +2

Many autonomous systems forecast aspects of the future in order to aid decision-making. For example, self-driving vehicles and robotic manipulation systems often forecast future ob…

cs.CV2019

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Nicholas Rhinehart, Rowan McAllister, Kris Kitani +1

For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other…

cs.CV2019

Generative Hybrid Representations for Activity Forecasting with No-Regret Learning

Jiaqi Guan, Ye Yuan, Kris M. Kitani +1

Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems. Part of this difficulty stems from lear…

cs.CV2016

First-Person Activity Forecasting with Online Inverse Reinforcement Learning

Nicholas Rhinehart, Kris M. Kitani

We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek…

cs.CV2016

Learning Action Maps of Large Environments via First-Person Vision

Nicholas Rhinehart, Kris M. Kitani

When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understandin…