From the 1 of 63 linked papers with an AI index.
180 citations · 844 across the 57 of their papers we have counts for
13 papers · 1 filter
Fast Point Transformer
Chunghyun Park, Yoonwoo Jeong, Minsu Cho +1
The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approach…
Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation
Jeongbeen Yoon, Dahyun Kang, Minsu Cho
Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain. In…
Relational Self-Attention: What's Missing in Attention for Video Understanding
Manjin Kim, Heeseung Kwon, Chunyu Wang +2
Convolution has been arguably the most important feature transform for modern neural networks, leading to the advance of deep learning. Recent emergence of Transformer networks, wh…
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Hyunsoo Chung, Jungtaek Kim, Boris Knyazev +4
Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possib…
Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence
Juhong Min, Seungwook Kim, Minsu Cho
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this wor…
Deep Hough Voting for Robust Global Registration
Junha Lee, Seungwook Kim, Minsu Cho +1
Point cloud registration is the task of estimating the rigid transformation that aligns a pair of point cloud fragments. We present an efficient and robust framework for pairwise r…