121 citations · 295 across the 22 of their papers we have counts for
22 papers
Soundini: Sound-Guided Diffusion for Natural Video Editing
Seung Hyun Lee, Sieun Kim, Innfarn Yoo +6
We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting. Animating the appearance of the visual effect is challenging beca…
FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment
Gyeongrok Oh, Sungjune Kim, Heon Gu +3
Moire patterns, created by the interference between overlapping grid patterns in the pixel space, degrade the visual quality of images and videos. Therefore, removing such patterns…
Ensuring Visual Commonsense Morality for Text-to-Image Generation
Seongbeom Park, Suhong Moon, Jinkyu Kim
Text-to-image generation methods produce high-resolution and high-quality images, but these methods should not produce immoral images that may contain inappropriate content from th…
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…
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…
Grounding Visual Representations with Texts for Domain Generalization
Seonwoo Min, Nokyung Park, Siwon Kim +2
Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natura…
Robust Sound-Guided Image Manipulation
Seung Hyun Lee, Gyeongrok Oh, Wonmin Byeon +3
Recent successes suggest that an image can be manipulated by a text prompt, e.g., a landscape scene on a sunny day is manipulated into the same scene on a rainy day driven by a tex…
An Embedding-Dynamic Approach to Self-supervised Learning
Suhong Moon, Domas Buracas, Seunghyun Park +2
A number of recent self-supervised learning methods have shown impressive performance on image classification and other tasks. A somewhat bewildering variety of techniques have bee…
ORA3D: Overlap Region Aware Multi-view 3D Object Detection
Wonseok Roh, Gyusam Chang, Seokha Moon +5
Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly, and the networks' understanding of the scene is often limited to that of…
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
Jinkyu Kim, Reza Mahjourian, Scott Ettinger +4
We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy.…
Sound-Guided Semantic Video Generation
Seung Hyun Lee, Gyeongrok Oh, Wonmin Byeon +7
The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent space is useful for realistic video generation. However, the generated motion in the video is usually n…
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…
Sound-Guided Semantic Image Manipulation
Seung Hyun Lee, Wonseok Roh, Wonmin Byeon +4
The recent success of the generative model shows that leveraging the multi-modal embedding space can manipulate an image using text information. However, manipulating an image with…
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
Francis Indaheng, Edward Kim, Kesav Viswanadha +4
Behavior prediction remains one of the most challenging tasks in the autonomous vehicle (AV) software stack. Forecasting the future trajectories of nearby agents plays a critical r…
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,…
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…
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…
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Raehyun Kim, Chan Ho So, Minbyul Jeong +3
Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices…
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention
Ye Xia, Jinkyu Kim, John Canny +2
Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the m…
Textual Explanations for Self-Driving Vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell +2
Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations wh…
Predicting Driver Attention in Critical Situations
Ye Xia, Danqing Zhang, Jinkyu Kim +3
Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so r…
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention
Jinkyu Kim, John Canny
Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret r…