Search Results for author:
Found 3 papers, 2 papers with code
Deep Representations for Time-varying Brain Datasets
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications.
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition
Automatic emotion recognition (AER) based on enriched multimodal inputs, including text, speech, and visual clues, is crucial in the development of emotionally intelligent machines.
U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina
The network consists of a novel bottleneck attention block that combines and refines self-attention, channel attention, and relative-position attention to highlight retinal abnormalities that may be important for fovea and OD segmentation in the degenerated retina.
Ranked #1 on
Fovea Detection
on REFUGE
