activity
20172023
most citedINTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

354 citations · 940 across the 112 of their papers we have counts for

collaborators
Showing 2021 · cs.CVShow all

8 papers · 2 filters

cs.CV2021★ 2 cited

Towards General and Efficient Active Learning

Yichen Xie, Masayoshi Tomizuka, Wei Zhan

Active learning selects the most informative samples to exploit limited annotation budgets. Existing work follows a cumbersome pipeline that repeats the time-consuming model traini…

cs.CV2021★ 3 cited

Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting

Rui Zhou, Hongyu Zhou, Huidong Gao +3

Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge. Recent advances in using data-driven approaches ha…

cs.CV2021★ 1 cited

RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting

Jiachen Li, Fan Yang, Hengbo Ma +3

Motion forecasting plays a significant role in various domains (e.g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of his…

cs.CV2021★ 4 cited

Spectral Temporal Graph Neural Network for Trajectory Prediction

Defu Cao, Jiachen Li, Hengbo Ma +1

An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobi…

cs.CV2021★ 1 cited

History Encoding Representation Design for Human Intention Inference

Zhuo Xu, Masayoshi Tomizuka

In this extended abstract, we investigate the design of learning representation for human intention inference. In our designed human intention prediction task, we propose a history…

cs.CV2021

Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models

Chenfeng Xu, Shijia Yang, Tomer Galanti +7

3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed fo…