activity
20172022
most citedOccupancy Flow Fields for Motion Forecasting in Autonomous Driving

74 citations · 131 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV2022

LISA: Localized Image Stylization with Audio via Implicit Neural Representation

Seung Hyun Lee, Chanyoung Kim, Wonmin Byeon +3

We present a novel framework, Localized Image Stylization with Audio (LISA) which performs audio-driven localized image stylization. Sound often provides information about the spec…

cs.CV20222 cited

Zero-shot Visual Commonsense Immorality Prediction

Yujin Jeong, Seongbeom Park, Suhong Moon +1

Artificial intelligence is currently powering diverse real-world applications. These applications have shown promising performance, but raise complicated ethical issues, i.e. how t…

cs.RO202274 cited

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

Reza Mahjourian, Jinkyu Kim, Yuning Chai +3

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving. Our representation is a spatio-temporal g…

cs.CV2021

SelfReg: Self-supervised Contrastive Regularization for Domain Generalization

Daehee Kim, Seunghyun Park, Jinkyu Kim +1

In general, an experimental environment for deep learning assumes that the training and the test dataset are sampled from the same distribution. However, in real-world situations,…

cs.CV20201 cited

Attentional Bottleneck: Towards an Interpretable Deep Driving Network

Jinkyu Kim, Mayank Bansal

Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they should prov…

cs.CV20191 cited

Grounding Human-to-Vehicle Advice for Self-driving Vehicles

Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen +2

Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human a…