60 citations · 77 across the 11 of their papers we have counts for
13 papers
Stable Diffusion Models are Secretly Good at Visual In-Context Learning
Trevine Oorloff, Vishwanath Sindagi, Wele Gedara Chaminda Bandara +4
Large language models (LLM) in natural language processing (NLP) have demonstrated great potential for in-context learning (ICL) -- the ability to leverage a few sets of example pr…
:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training
Nisarg A. Shah, Wele Gedara Chaminda Bandara, Shameema Skider +2
Automated analysis of surgical videos is crucial for improving surgical training, workflow optimization, and postoperative assessment. We introduce a CSMAE, Masked Autoencoder (MAE…
Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modeling
Wele Gedara Chaminda Bandara, Vishal M. Patel
In this paper, we introduce Attention Prompt Tuning (APT) - a computationally efficient variant of prompt tuning for video-based applications such as action recognition. Prompt tun…
I-Diff: Structural Regularization for High-Fidelity Diffusion Models
Shakthi Perera, Dilum Fernando, H. L. P. Malshan +5
Denoising Diffusion Probabilistic Models (DDPMs) have significantly advanced generative AI, achieving impressive results in high-quality image and data generation. However, enhanci…
Guarding Barlow Twins Against Overfitting with Mixed Samples
Wele Gedara Chaminda Bandara, Celso M. De Melo, Vishal M. Patel
Self-supervised Learning (SSL) aims to learn transferable feature representations for downstream applications without relying on labeled data. The Barlow Twins algorithm, renowned…
AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami +3
Masked Autoencoders (MAEs) learn generalizable representations for image, text, audio, video, etc., by reconstructing masked input data from tokens of the visible data. Current MAE…