28 citations · 31 across the 2 of their papers we have counts for
8 papers · 1 filter
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…
Semantics to Space(S2S): Embedding semantics into spatial space for zero-shot verb-object query inferencing
Sungmin Eum, Heesung Kwon
We present a novel deep zero-shot learning (ZSL) model for inferencing human-object-interaction with verb-object (VO) query. While the previous two-stream ZSL approaches only use t…
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…
Object and Text-guided Semantics for CNN-based Activity Recognition
Sungmin Eum, Christopher Reale, Heesung Kwon +2
Many previous methods have demonstrated the importance of considering semantically relevant objects for carrying out video-based human activity recognition, yet none of the methods…