Search Results for author: Jihwan Park

Found 7 papers, 4 papers with code

Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection

1 code implementation26 Mar 2024 Jongha Kim, Jihwan Park, Jinyoung Park, Jinyoung Kim, Sehyung Kim, Hyunwoo J. Kim

Groupwise Query Specialization trains a specialized query by dividing queries and relations into disjoint groups and directing a query in a specific query group solely toward relations in the corresponding relation group.

Relation Relationship Detection +1

Metric Learning for User-defined Keyword Spotting

no code implementations1 Nov 2022 Jaemin Jung, Youkyum Kim, Jihwan Park, Youshin Lim, Byeong-Yeol Kim, Youngjoon Jang, Joon Son Chung

In particular, we make the following contributions: (1) we construct a large-scale keyword dataset with an existing speech corpus and propose a filtering method to remove data that degrade model training; (2) we propose a metric learning-based two-stage training strategy, and demonstrate that the proposed method improves the performance on the user-defined keyword spotting task by enriching their representations; (3) to facilitate the fair comparison in the user-defined KWS field, we propose unified evaluation protocol and metrics.

Keyword Spotting Metric Learning

Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection

1 code implementation CVPR 2022 Jihwan Park, Seungjun Lee, Hwan Heo, Hyeong Kyu Choi, Hyunwoo J. Kim

Motivated by various inference paths for HOI detection, we propose cross-path consistency learning (CPC), which is a novel end-to-end learning strategy to improve HOI detection for transformers by leveraging augmented decoding paths.

Human-Object Interaction Detection object-detection +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

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