no code implementations • 18 Mar 2024 • Mincheol Chang, Siyeong Lee, Jinkyu Kim, Namil Kim
Typical LiDAR-based 3D object detection models are trained in a supervised manner with real-world data collection, which is often imbalanced over classes (or long-tailed).
1 code implementation • 27 Feb 2024 • Tyler L. Hayes, César R. de Souza, Namil Kim, Jiwon Kim, Riccardo Volpi, Diane Larlus
In this work, we look at ways to extend a detector trained for a set of base classes so it can i) spot the presence of novel classes, and ii) automatically enrich its repertoire to be able to detect those newly discovered classes together with the base ones.
no code implementations • 12 Oct 2023 • Sukwoong Choi, Hyo Kang, Namil Kim, Junsik Kim
We study how humans learn from AI, exploiting an introduction of an AI-powered Go program (APG) that unexpectedly outperformed the best professional player.
1 code implementation • journal 2022 • Daechan Han, Jeongmin Shin, Namil Kim, Soomnim Hwang, Yukyung Choi
Recently, transformers have been widely adopted for various computer vision tasks and show promising results due to their ability to encode long-range spatial dependencies in an image effectively.
Ranked #3 on Monocular Depth Estimation on DDAD
1 code implementation • IEEE ROBOTICS AND AUTOMATION LETTERS 2021 • Jiwon Kim, Hyeongjun Kim, Taejoo Kim, Namil Kim, Yukyung Choi
In this letter, we tackle multispectral pedestrian detection, where all input data are not paired.
1 code implementation • ICCV 2019 • Seungmin Lee, Dongwan Kim, Namil Kim, Seong-Gyun Jeong
Recent works on domain adaptation exploit adversarial training to obtain domain-invariant feature representations from the joint learning of feature extractor and domain discriminator networks.
Ranked #18 on Domain Adaptation on VisDA2017
3 code implementations • ICCV 2017 • Seokju Lee, Junsik Kim, Jae Shin Yoon, Seunghak Shin, Oleksandr Bailo, Namil Kim, Tae-Hee Lee, Hyun Seok Hong, Seung-Hoon Han, In So Kweon
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions.
Ranked #1 on Lane Detection on Caltech Lanes Washington
no code implementations • 24 Mar 2016 • Youngjin Yoon, Gyeongmin Choe, Namil Kim, Joon-Young Lee, In So Kweon
We present surface normal estimation using a single near infrared (NIR) image.
1 code implementation • 24 Mar 2016 • Donggeun Yoo, Namil Kim, Sunggyun Park, Anthony S. Paek, In So Kweon
We present an image-conditional image generation model.
no code implementations • CVPR 2015 • Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, In So Kweon
With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs.