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Found 10 papers, 5 papers with code
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
The spatio-temporal complexity of video data presents significant challenges in tasks such as compression, generation, and inpainting.
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning.
D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods
This makes it easy to move features from the style image to the content image without bias.
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
We also collected CT scans of 1, 350 patients from publicly available sources for benchmarking purposes.
Ethical Framework for Responsible Foundational Models in Medical Imaging
Foundational models (FMs) have tremendous potential to revolutionize medical imaging.
MOVES: Movable and Moving LiDAR Scene Segmentation in Label-Free settings using Static Reconstruction
In the real world however, LiDAR scans consist of non-stationary dynamic structures - moving and movable objects.
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks
We introduce a novel network, GAFNet (Global Attention Fourier Net), which learns through large-scale pre-training over three image-text datasets (COCO, SBU, and CC-3M), for achieving high performance on downstream vision and language tasks.
TPFNet: A Novel Text In-painting Transformer for Text Removal
Further, we use a novel multi-headed decoder that generates a high-pass filtered image and a segmentation map, in addition to a text-free image.
ACLNet: An Attention and Clustering-based Cloud Segmentation Network
We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images.
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