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Found 6 papers, 2 papers with code
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Medical image segmentation - the prerequisite of numerous clinical needs - has been significantly prospered by recent advances in convolutional neural networks (CNNs).
Ranked #16 on
Medical Image Segmentation
on ETIS-LARIBPOLYPDB
Improve Model Generalization and Robustness to Dataset Bias with Bias-regularized Learning and Domain-guided Augmentation
In this study, we investigated model robustness to dataset bias using three large-scale Chest X-ray datasets: first, we assessed the dataset bias using vanilla training baseline; second, we proposed a novel multi-source domain generalization model by (a) designing a new bias-regularized loss function; and (b) synthesizing new data for domain augmentation.
Robust and High Performance Face Detector
In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks.
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining
A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image.
Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation
First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.
Hello Edge: Keyword Spotting on Microcontrollers
We train various neural network architectures for keyword spotting published in literature to compare their accuracy and memory/compute requirements.
Ranked #14 on
Keyword Spotting
on Google Speech Commands
