95 citations · 159 across the 17 of their papers we have counts for
18 papers
Scale-Wise VAR is Secretly Discrete Diffusion
Amandeep Kumar, Nithin Gopalakrishnan Nair, Vishal M. Patel
Autoregressive (AR) transformers have emerged as a powerful paradigm for visual generation, largely due to their scalability, computational efficiency and unified architecture with…
Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data
Nithin Gopalakrishnan Nair, Srinivas Kaza, Xuan Luo +3
Large transformer-based models have made significant progress in generalizable novel view synthesis (NVS) from sparse input views, generating novel viewpoints without the need for…
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition
Kartik Narayan, Nithin Gopalakrishnan Nair, Jennifer Xu +2
Pre-training on large-scale datasets and utilizing margin-based loss functions have been highly successful in training models for high-resolution face recognition. However, these m…
GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration
Sudarshan Rajagopalan, Nithin Gopalakrishnan Nair, Jay N. Paranjape +1
Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor…
Dreamguider: Improved Training free Diffusion-based Conditional Generation
Nithin Gopalakrishnan Nair, Vishal M Patel
Diffusion models have emerged as a formidable tool for training-free conditional generation.However, a key hurdle in inference-time guidance techniques is the need for compute-heav…
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M Patel
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation. For most ap…
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M. Patel
Recently, diffusion transformers have gained wide attention with its excellent performance in text-to-image and text-to-vidoe models, emphasizing the need for transformers as backb…
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit +4
Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in designing models…
AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation
Jay N. Paranjape, Nithin Gopalakrishnan Nair, Shameema Sikder +2
Segmentation is a fundamental problem in surgical scene analysis using artificial intelligence. However, the inherent data scarcity in this domain makes it challenging to adapt tra…
: 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…
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors
Kangfu Mei, Nithin Gopalakrishnan Nair, Vishal M. Patel
Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentime…
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…
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
Although many long-range imaging systems are designed to support extended vision applications, a natural obstacle to their operation is degradation due to atmospheric turbulence. A…
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Vishal M. Patel
Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using…
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…
SAR Despeckling using a Denoising Diffusion Probabilistic Model
Malsha V. Perera, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara +1
Speckle is a multiplicative noise which affects all coherent imaging modalities including Synthetic Aperture Radar (SAR) images. The presence of speckle degrades the image quality…
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…
A comparison of different atmospheric turbulence simulation methods for image restoration
Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to…