986 citations · 1.1k across the 22 of their papers we have counts for
29 papers
MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Rendering
Jaehoon Choi, Yonghan Lee, Hyungtae Lee +2
Recently, 3D Gaussian splatting has gained attention for its capability to generate high-fidelity rendering results. At the same time, most applications such as games, animation, a…
Exploring the Potential of Synthetic Data to Replace Real Data
Hyungtae Lee, Yan Zhang, Heesung Kwon +1
The potential of synthetic data to replace real data creates a huge demand for synthetic data in data-hungry AI. This potential is even greater when synthetic data is used for trai…
SynPlay: Large-Scale Synthetic Human Data with Real-World Diversity for Aerial-View Perception
Jinsub Yim, Hyungtae Lee, Sungmin Eum +4
We introduce SynPlay, a large-scale synthetic human dataset purpose-built for advancing multi-perspective human localization, with a predominant focus on aerial-view perception. Sy…
Exploring the Impact of Synthetic Data for Aerial-view Human Detection
Hyungtae Lee, Yan Zhang, Yi-Ting Shen +2
Aerial-view human detection has a large demand for large-scale data to capture more diverse human appearances compared to ground-view human detection. Therefore, synthetic data can…
Diversifying Human Pose in Synthetic Data for Aerial-view Human Detection
Yi-Ting Shen, Hyungtae Lee, Heesung Kwon +1
Synthetic data generation has emerged as a promising solution to the data scarcity issue in aerial-view human detection. However, creating datasets that accurately reflect varying…
TK-Planes: Tiered K-Planes with High Dimensional Feature Vectors for Dynamic UAV-based Scenes
Christopher Maxey, Jaehoon Choi, Yonghan Lee +3
In this paper, we present a new approach to bridge the domain gap between synthetic and real-world data for unmanned aerial vehicle (UAV)-based perception. Our formulation is desig…
UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception
Christopher Maxey, Jaehoon Choi, Hyungtae Lee +2
Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. U…
Novel Categories Discovery Via Constraints on Empirical Prediction Statistics
Zahid Hasan, Abu Zaher Md Faridee, Masud Ahmed +4
Novel Categories Discovery (NCD) aims to cluster novel data based on the class semantics of known classes using the open-world partial class space annotated dataset. As an alternat…
NEV-NCD: Negative Learning, Entropy, and Variance regularization based novel action categories discovery
Zahid Hasan, Masud Ahmed, Abu Zaher Md Faridee +4
Novel Categories Discovery (NCD) facilitates learning from a partially annotated label space and enables deep learning (DL) models to operate in an open-world setting by identifyin…
Progressive Transformation Learning for Leveraging Virtual Images in Training
Yi-Ting Shen, Hyungtae Lee, Heesung Kwon +1
To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instance…
Negative Samples are at Large: Leveraging Hard-distance Elastic Loss for Re-identification
Hyungtae Lee, Sungmin Eum, Heesung Kwon
We present a Momentum Re-identification (MoReID) framework that can leverage a very large number of negative samples in training for general re-identification task. The design of t…
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification
Hyungtae Lee, Sungmin Eum, Heesung Kwon
A pretrain-finetune strategy is widely used to reduce the overfitting that can occur when data is insufficient for CNN training. First few layers of a CNN pretrained on a large-sca…
DBF: Dynamic Belief Fusion for Combining Multiple Object Detectors
Hyungtae Lee, Heesung Kwon
In this paper, we propose a novel and highly practical score-level fusion approach called dynamic belief fusion (DBF) that directly integrates inference scores of individual detect…
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation
Hyungtae Lee, Heesung Kwon
Recently, self-supervised learning has attracted attention due to its remarkable ability to acquire meaningful representations for classification tasks without using semantic label…
S-DOD-CNN: Doubly Injecting Spatially-Preserved Object Information for Event Recognition
Hyungtae Lee, Sungmin Eum, Heesung Kwon
We present a novel event recognition approach called Spatially-preserved Doubly-injected Object Detection CNN (S-DOD-CNN), which incorporates the spatially preserved object detecti…
Is Pretraining Necessary for Hyperspectral Image Classification?
Hyungtae Lee, Sungmin Eum, Heesung Kwon
We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the…
Generating Hard Examples for Pixel-wise Classification
Hyungtae Lee, Heesung Kwon, Wonkook Kim
Pixel-wise classification in remote sensing identifies entities in large-scale satellite-based images at the pixel level. Few fully annotated large-scale datasets for pixel-wise cl…
DOD-CNN: Doubly-injecting Object Information for Event Recognition
Hyungtae Lee, Sungmin Eum, Heesung Kwon
Recognizing an event in an image can be enhanced by detecting relevant objects in two ways: 1) indirectly utilizing object detection information within the unified architecture or…
Cross-domain CNN for Hyperspectral Image Classification
Hyungtae Lee, Sungmin Eum, Heesung Kwon
In this paper, we address the dataset scarcity issue with the hyperspectral image classification. As only a few thousands of pixels are available for training, it is difficult to e…
ME R-CNN: Multi-Expert R-CNN for Object Detection
Hyungtae Lee, Sungmin Eum, Heesung Kwon
We introduce Multi-Expert Region-based Convolutional Neural Network (ME R-CNN) which is equipped with multiple experts (ME) where each expert is learned to process a certain type o…
IOD-CNN: Integrating Object Detection Networks for Event Recognition
Sungmin Eum, Hyungtae Lee, Heesung Kwon +1
Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of…
Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification
Yilun Cao, Hyungtae Lee, Heesung Kwon
In this paper, we introduce a novel fusion method that can enhance object detection performance by fusing decisions from two different types of computer vision tasks: object detect…
Exploitation of Semantic Keywords for Malicious Event Classification
Hyungtae Lee, Sungmin Eum, Joel Levis +3
Learning an event classifier is challenging when the scenes are semantically different but visually similar. However, as humans, we typically handle such tasks painlessly by adding…
DTM: Deformable Template Matching
Hyungtae Lee, Heesung Kwon, Ryan M. Robinson +1
A novel template matching algorithm that can incorporate the concept of deformable parts, is presented in this paper. Unlike the deformable part model (DPM) employed in object reco…