Search Results for author:
Found 6 papers, 1 papers with code
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
This paper presents a novel approach that synergies spatial and spectral representations to enhance domain-generalized medical image segmentation.
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.
Accelerating Gradient-based Meta Learner
Meta Learning has been in focus in recent years due to the meta-learner model's ability to adapt well and generalize to new tasks, thus, reducing both the time and data requirements for learning.
Fast Online "Next Best Offers" using Deep Learning
In this paper, we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting.
Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Modern real-time business analytic consist of heterogeneous workloads (e. g, database queries, graph processing, and machine learning).
