no code implementations • 16 Nov 2023 • Jinyoung Park, Ameen Patel, Omar Zia Khan, Hyunwoo J. Kim, Joo-Kyung Kim
Specifically, we first leverage LLMs to construct a "question/rationale graph" by using knowledge extraction prompting given the initial question and the rationales generated in the previous steps.
no code implementations • 2 Apr 2023 • Sangmin Woo, So-Yeong Jeon, Jinyoung Park, Minji Son, Sumin Lee, Changick Kim
We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch.
1 code implementation • CVPR 2023 • Jinyoung Park, Sanghyeok Lee, Sihyeon Kim, Yunyang Xiong, Hyunwoo J. Kim
In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity.
Ranked #2 on
3D Part Segmentation
on ShapeNet-Part
1 code implementation • 7 Dec 2022 • Jinyoung Park, Minseok Son, Seungju Cho, Inyoung Lee, Changick Kim
This paper presents a solution to the Weather4cast 2022 Challenge Stage 2.
1 code implementation • 2 Dec 2022 • Jinyoung Park, Hyeong Kyu Choi, Juyeon Ko, Hyeonjin Park, Ji-Hoon Kim, Jisu Jeong, KyungMin Kim, Hyunwoo J. Kim
To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner.
no code implementations • 29 Jun 2022 • Jinyoung Park, Seongjun Yun, Hyeonjin Park, Jaewoo Kang, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision.
no code implementations • NeurIPS 2021 • Hyeonjin Park, Seunghun Lee, Sihyeon Kim, Jinyoung Park, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim
We also propose a simple and effective semi-supervised learning strategy with generated samples from MH-Aug. Our extensive experiments demonstrate that MH-Aug can generate a sequence of samples according to the target distribution to significantly improve the performance of GNNs.
1 code implementation • 25 Jan 2022 • Sangmin Woo, Jinyoung Park, Inyong Koo, Sumin Lee, Minki Jeong, Changick Kim
To our surprise, we found that training schedule shows divide-and-conquer-like pattern: time segments are first diversified regardless of the target, then coupled with each target, and fine-tuned to the target again.
1 code implementation • 29 Dec 2021 • Jinyoung Park, Sungdong Yoo, Jihwan Park, Hyunwoo J. Kim
To address the two common problems of graph convolution, in this paper, we propose Deformable Graph Convolutional Networks (Deformable GCNs) that adaptively perform convolution in multiple latent spaces and capture short/long-range dependencies between nodes.
Ranked #3 on
Node Classification on Non-Homophilic (Heterophilic) Graphs
on Cornell (48%/32%/20% fixed splits)
Node Classification on Non-Homophilic (Heterophilic) Graphs
Representation Learning
3 code implementations • 30 Nov 2021 • Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park
Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.
Ranked #10 on
Document Image Classification
on RVL-CDIP
1 code implementation • 1 Mar 2021 • Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim
Our method is learning to learn a primary task with various auxiliary tasks to improve generalization performance.
no code implementations • 24 Oct 2020 • Bryce Chudomelka, Youngjoon Hong, Hyunwoo Kim, Jinyoung Park
Nonlinear differential equations are challenging to solve numerically and are important to understanding the dynamics of many physical systems.
1 code implementation • ECCV 2020 • Byungjoo Kim, Bryce Chudomelka, Jinyoung Park, Jaewoo Kang, Youngjoon Hong, Hyunwoo J. Kim
Motivated by the SSP property and a generalized Runge-Kutta method, we propose Strong Stability Preserving networks (SSP networks) which improve robustness against adversarial attacks.
1 code implementation • NeurIPS 2020 • Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim
Our proposed method is learning to learn a primary task by predicting meta-paths as auxiliary tasks.
no code implementations • 10 Sep 2019 • Jeff Kahn, Jinyoung Park
Let $Q_n$ be the $n$-dimensional Hamming cube and $N=2^n$.
Combinatorics 05C69