70 citations · 204 across the 18 of their papers we have counts for
5 papers · 1 filter
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed +3
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is…
Fine-tuned CLIP Models are Efficient Video Learners
Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz +2
Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches fo…
MaPLe: Multi-modal Prompt Learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz +2
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text promp…
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Hanoona Rasheed, Muhammad Maaz, Muhammad Uzair Khattak +2
Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at infe…
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
Muhammad Maaz, Abdelrahman Shaker, Hisham Cholakkal +4
In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot…