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Found 6 papers, 5 papers with code
MedContext: Learning Contextual Cues for Efficient Volumetric Medical Segmentation
The proposed approach induces contextual knowledge in the network by learning to reconstruct the missing organ or parts of an organ in the output segmentation space.
Multi-Attribute Vision Transformers are Efficient and Robust Learners
Since their inception, Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) across a wide spectrum of tasks.
Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization
The promising zero-shot generalization of vision-language models such as CLIP has led to their adoption using prompt learning for numerous downstream tasks.
LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts
Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects.
How to Train Vision Transformer on Small-scale Datasets?
However, in contrast to convolutional neural networks, Vision Transformer lacks inherent inductive biases.
A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
The modern day scenario, where security is of prime concern, regular face identification techniques do not perform as required when the faces are disguised, which calls for a different approach to handle situations where intruders have their faces masked.
