28 citations · 63 across the 7 of their papers we have counts for
6 papers · 1 filter
SENSE: Semantically Enhanced Node Sequence Embedding
Swati Rallapalli, Liang Ma, Mudhakar Srivatsa +4
Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single grap…
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach
Zhenyu Wu, Karthik Suresh, Priya Narayanan +3
Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained…
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…
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification
Colin Samplawski, Heesung Kwon, Erik Learned-Miller +1
Zero-shot learning (ZSL) is one of the most extreme forms of learning from scarce labeled data. It enables predicting that images belong to classes for which no labeled training in…
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…