Deep-learning-based object detection methods show promise for improving
...
This work reveals undiscovered challenges in the performance and
general...
Medical images come in high resolutions. A high resolution is vital for
...
Deep learning relies on the availability of a large corpus of data (labe...
A neural network regularizer (e.g., weight decay) boosts performance by
...
Retrieval networks are essential for searching and indexing. Compared to...
We introduce an unsupervised formulation to estimate heteroscedastic
unc...
We employ triplet loss as a space embedding regularizer to boost
classif...
We cast visual retrieval as a regression problem by posing triplet loss ...
Semantic image segmentation plays an important role in modeling
patient-...
We present a self-supervised approach using spatio-temporal signals betw...
While regulators advocate for higher cloud transparency, many Cloud Serv...
We propose a recurrent variational auto-encoder for texture synthesis. A...
In this paper, we cast the scribble-based interactive image segmentation...