Search Results for author:
Found 6 papers, 2 papers with code
DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception.
Ranked #15 on
Thermal Image Segmentation
on MFN Dataset
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining
We propose the Graph Context Encoder (GCE), a simple but efficient approach for graph representation learning based on graph feature masking and reconstruction.
Realistic molecule optimization on a learned graph manifold
Deep learning based molecular graph generation and optimization has recently been attracting attention due to its great potential for de novo drug design.
Graph convolutions that can finally model local structure
Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles.
Ranked #13 on
Graph Property Prediction
on ogbg-molpcba
Iterative energy-based projection on a normal data manifold for anomaly localization
Indeed, an autoencoder trained on normal data is expected to only be able to reconstruct normal features of the data, allowing the segmentation of anomalous pixels in an image via a simple comparison between the image and its autoencoder reconstruction.
Ranked #81 on
Anomaly Detection
on MVTec AD
(Segmentation AUROC metric)
Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer
This paper presents a novel unsupervised method to transfer the style of an example image to a source image.
