23 citations · 24 across the 6 of their papers we have counts for
6 papers
VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception
Jiyoung Lee, Seungho Kim, Seunghyun Won +6
AI alignment refers to models acting towards human-intended goals, preferences, or ethical principles. Given that most large-scale deep learning models act as black boxes and canno…
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud
Jaeseok Choi, Yeji Song, Nojun Kwak
Data augmentation has greatly contributed to improving the performance in image recognition tasks, and a lot of related studies have been conducted. However, data augmentation on 3…
KL-Divergence-Based Region Proposal Network for Object Detection
Geonseok Seo, Jaeyoung Yoo, Jaeseok Choi +1
The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task. Howe…
Genetic-Gated Networks for Deep Reinforcement
Simyung Chang, John Yang, Jaeseok Choi +1
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method ca…
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning
Simyung Chang, YoungJoon Yoo, Jaeseok Choi +1
We introduce a novel method to train agents of reinforcement learning (RL) by sharing knowledge in a way similar to the concept of using a book. The recorded information in the for…
Residual Features and Unified Prediction Network for Single Stage Detection
Kyoungmin Lee, Jaeseok Choi, Jisoo Jeong +1
Recently, a lot of single stage detectors using multi-scale features have been actively proposed. They are much faster than two stage detectors that use region proposal networks (R…