28 citations · 57 across the 4 of their papers we have counts for
11 papers
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…
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…