515 citations · 739 across the 33 of their papers we have counts for
33 papers
UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection
Bumsoo Kim, Taeho Choi, Jaewoo Kang +1
Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully under…
Semantic-Aware Implicit Template Learning via Part Deformation Consistency
Sihyeon Kim, Minseok Joo, Jaewon Lee +3
Learning implicit templates as neural fields has recently shown impressive performance in unsupervised shape correspondence. Despite the success, we observe current approaches, whi…
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Dohwan Ko, Joonmyung Choi, Hyeong Kyu Choi +3
Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase,…
Semantic-aware Occlusion Filtering Neural Radiance Fields in the Wild
Jaewon Lee, Injae Kim, Hwan Heo +1
We present a learning framework for reconstructing neural scene representations from a small number of unconstrained tourist photos. Since each image contains transient occluders,…
VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression
Won Jo, Geuntaek Lim, Gwangjin Lee +3
In content-based video retrieval (CBVR), dealing with large-scale collections, efficiency is as important as accuracy; thus, several video-level feature-based studies have actively…
Domain Generalization Emerges from Dreaming
Hwan Heo, Youngjin Oh, Jaewon Lee +1
Recent studies have proven that DNNs, unlike human vision, tend to exploit texture information rather than shape. Such texture bias is one of the factors for the poor generalizatio…
Relation-Aware Language-Graph Transformer for Question Answering
Jinyoung Park, Hyeong Kyu Choi, Juyeon Ko +5
Question Answering (QA) is a task that entails reasoning over natural language contexts, and many relevant works augment language models (LMs) with graph neural networks (GNNs) to…
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim
Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency. However, many salien…
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Seongjun Yun, Seoyoon Kim, Junhyun Lee +2
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic…
Deformable Graph Transformer
Jinyoung Park, Seongjun Yun, Hyeonjin Park +5
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the succes…
Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective
Sungmin Cha, Jihwan Kwak, Dongsub Shim +4
Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data while not forgetting past learned classes. The common evalu…
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection
Jihwan Park, SeungJun Lee, Hwan Heo +2
Human-Object Interaction detection is a holistic visual recognition task that entails object detection as well as interaction classification. Previous works of HOI detection has be…
Perception Prioritized Training of Diffusion Models
Jooyoung Choi, Jungbeom Lee, Chaehun Shin +3
Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i.e., denoising sco…
Video-Text Representation Learning via Differentiable Weak Temporal Alignment
Dohwan Ko, Joonmyung Choi, Juyeon Ko +4
Learning generic joint representations for video and text by a supervised method requires a prohibitively substantial amount of manually annotated video datasets. As a practical al…
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Hyeonjin Park, Seunghun Lee, Sihyeon Kim +5
Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a…
Deformable Graph Convolutional Networks
Jinyoung Park, Sungdong Yoo, Jihwan Park +1
Graph neural networks (GNNs) have significantly improved the representation power for graph-structured data. Despite of the recent success of GNNs, the graph convolution in most GN…
VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation
Su Ho Han, Sukjun Hwang, Seoung Wug Oh +4
For online video instance segmentation (VIS), fully utilizing the information from previous frames in an efficient manner is essential for real-time applications. Most previous met…
Point Cloud Augmentation with Weighted Local Transformations
Sihyeon Kim, Sanghyeok Lee, Dasol Hwang +3
Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach…
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Seongjun Yun, Minbyul Jeong, Sungdong Yoo +5
Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing G…
HOTR: End-to-End Human-Object Interaction Detection with Transformers
Bumsoo Kim, Junhyun Lee, Jaewoo Kang +2
Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i.e., humans) and target (…
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
In recent years, graph neural networks (GNNs) have been widely adopted in the representation learning of graph-structured data and provided state-of-the-art performance in various…
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
Zheda Mai, Ruiwen Li, Hyunwoo Kim +1
Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data w…
Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
Sam Sattarzadeh, Mahesh Sudhakar, Konstantinos N. Plataniotis +3
Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image…
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks
Mahesh Sudhakar, Sam Sattarzadeh, Konstantinos N. Plataniotis +3
Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models. Recently, perturba…