Search Results for author: Jinyoung Park

Found 15 papers, 9 papers with code

Graph-Guided Reasoning for Multi-Hop Question Answering in Large Language Models

no code implementations16 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.

Multi-hop Question Answering Question Answering

Sketch-based Video Object Localization

no code implementations2 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.

Object Localization set matching

Self-positioning Point-based Transformer for Point Cloud Understanding

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.

3D Part Segmentation 3D Point Cloud Classification +1

Relation-Aware Language-Graph Transformer for Question Answering

1 code implementation2 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.

Question Answering

Deformable Graph Transformer

no code implementations29 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.

Representation Learning

Metropolis-Hastings Data Augmentation for Graph Neural Networks

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.

Data Augmentation

Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos

1 code implementation25 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.

Natural Language Queries Temporal Localization +1

Deformable Graph Convolutional Networks

1 code implementation29 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.

Node Classification on Non-Homophilic (Heterophilic) Graphs Representation Learning

OCR-free Document Understanding Transformer

3 code implementations30 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.

Document Image Classification document understanding +3

Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation

no code implementations24 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.

Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods

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.

The number of maximal independent sets in the Hamming cube

no code implementations10 Sep 2019 Jeff Kahn, Jinyoung Park

Let $Q_n$ be the $n$-dimensional Hamming cube and $N=2^n$.

Combinatorics 05C69

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