We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales.
The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of the network.
Ranked #13 on Medical Image Segmentation on Kvasir-SEG
Our methods are based on the hypothesis that handwritten text images have specific spatial regions which are more unique to a writer's style, multi-scale features propagate characteristic features with respect to individual writers and patch-based features give more general and robust representations that helps to discriminate handwriting from different writers.
The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively.
Deep metric learning (ML) uses a carefully designed loss function to learn distance metrics for improving the discriminatory ability for tasks like clustering and retrieval.
Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc.
Our approach is capable of generating images that are very accurately aligned to the exhaustive textual descriptions of faces with many fine detail features of the face and helps in generating better images.
Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers).
The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets.
Ranked #3 on Medical Image Segmentation on 2018 Data Science Bowl
Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms.
A Zero-shot learning algorithm is capable of handling unseen classes, provided the algorithm has been fortified with rich discriminating features and reliable “attribute description” per class during training.