1 code implementation • 11 Mar 2024 • Wele Gedara Chaminda Bandara, Vishal M. Patel
This approach greatly reduces the number of learnable parameters compared to full tuning.
1 code implementation • 4 Dec 2023 • 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.
Ranked #1 on Self-Supervised Learning on STL-10
1 code implementation • 22 Mar 2023 • Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
Furthermore, as the intermediate time steps of the diffusion process are noisy, we incorporate a regression branch for direct crowd estimation only during training to improve the feature learning.
1 code implementation • 16 Mar 2023 • Wele Gedara Chaminda Bandara, Vishal M. Patel
This loss is motivated by the principle of metric learning where we simultaneously maximize the distance between change pair-wise pixels while minimizing the distance between no-change pair-wise pixels in bi-temporal image domain and their deep feature domain.
1 code implementation • CVPR 2023 • Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
We also introduce a novel reliability parameter that allows using different off-the-shelf diffusion models trained across various datasets during sampling time alone to guide it to the desired outcome satisfying multiple constraints.
Ranked #1 on Face Sketch Synthesis on Multi-Modal CelebA-HQ
2 code implementations • CVPR 2023 • Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami, Mehdi Nikkhah, Motilal Agrawal, Vishal M. Patel
Our adaptive masking strategy samples visible tokens based on the semantic context using an auxiliary sampling network.
Ranked #1 on Action Classification on Something-Something V2
1 code implementation • 23 Jun 2022 • Wele Gedara Chaminda Bandara, Nithin Gopalakrishnan Nair, Vishal M. Patel
However, in this work, our focus is not on image synthesis but on utilizing it as a pre-trained feature extractor for the downstream application of change detection.
Ranked #1 on Change Detection on DSIFN-CD
no code implementations • 16 Jun 2022 • Aimon Rahman, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu, Vishal M Patel
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the success of ultrasound-guided computer-assisted surgical procedures.
no code implementations • 10 Jun 2022 • Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M Patel
Based on the fact that the distribution over each time step in the diffusion model is Gaussian, in this work we show that there exists a closed-form expression to the generate the image corresponds to the given modalities.
1 code implementation • 9 Jun 2022 • Malsha V. Perera, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
The despeckled image is recovered by a reverse process which iteratively predicts the added noise using a noise predictor which is conditioned on the speckled image.
1 code implementation • 31 May 2022 • Malsha V. Perera, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel
We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.
1 code implementation • 18 Apr 2022 • Wele Gedara Chaminda Bandara, Vishal M. Patel
The performance of existing deep supervised CD methods is attributed to the large amounts of annotated data used to train the networks.
Ranked #3 on Semi-supervised Change Detection on LEVIR-CD - 5% labeled data (IoU metric)
1 code implementation • CVPR 2022 • Wele Gedara Chaminda Bandara, Vishal M. Patel
Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions.
1 code implementation • 23 Jan 2022 • Malsha V. Perera, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel
Synthetic Aperture Radar (SAR) images are usually degraded by a multiplicative noise known as speckle which makes processing and interpretation of SAR images difficult.
3 code implementations • 4 Jan 2022 • 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.
Ranked #15 on Change Detection on LEVIR-CD
1 code implementation • 16 Sep 2021 • Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel
Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image which is essential to extract road connectivity.
Ranked #1 on Road Segmentation on DeepGlobe
1 code implementation • 6 Jul 2021 • Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel
To estimate the PAN image of the up-sampled HSI, we also propose a learnable spectral response function (SRF).
Ranked #1 on Image Super-Resolution on Chikusei Dataset