95 citations · 265 across the 21 of their papers we have counts for
17 papers · 1 filter
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…
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…
Deep Metric Learning for Unsupervised Remote Sensing Change Detection
Wele Gedara Chaminda Bandara, Vishal M. Patel
Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs), which aids in various RS applications such as land cove…
: Multi-hypothesis Crowd Density Estimation using Diffusion Models
Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara +1
Crowd counting is a fundamental problem in crowd analysis which is typically accomplished by estimating a crowd density map and summing over the density values. However, this appro…
Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
Generating photos satisfying multiple constraints find broad utility in the content creation industry. A key hurdle to accomplishing this task is the need for paired data consistin…
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…
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M Patel
Image synthesis under multi-modal priors is a useful and challenging task that has received increasing attention in recent years. A major challenge in using generative models to ac…
DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection
Wele Gedara Chaminda Bandara, Nithin Gopalakrishnan Nair, Vishal M. Patel
Remote sensing change detection is crucial for understanding the dynamics of our planet's surface, facilitating the monitoring of environmental changes, evaluating human impact, pr…
SAR Despeckling Using Overcomplete Convolutional Networks
Malsha V. Perera, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu +1
Synthetic Aperture Radar (SAR) despeckling is an important problem in remote sensing as speckle degrades SAR images, affecting downstream tasks like detection and segmentation. Rec…
Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images
Wele Gedara Chaminda Bandara, Vishal M. Patel
Remote-sensing (RS) Change Detection (CD) aims to detect "changes of interest" from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attr…
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening
Wele Gedara Chaminda Bandara, Vishal M. Patel
Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral a…
Transformer-based SAR Image Despeckling
Malsha V. Perera, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu +1
Synthetic Aperture Radar (SAR) images are usually degraded by a multiplicative noise known as speckle which makes processing and interpretation of SAR images difficult. In this pap…
A Transformer-Based Siamese Network for Change Detection
Wele Gedara Chaminda Bandara, Vishal M. Patel
This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD) from a pair of co-registered remote sensing images. Dif…
SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel
Road extraction is an essential step in building autonomous navigation systems. Detecting road segments is challenging as they are of varying widths, bifurcated throughout the imag…
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction
Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel
Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-HSI) with a registered panchromatic image (PAN) to generate an enhanced HSI with high spectr…