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20222026
most citedDDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection

33 citations · 33 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers

Anh Nguyen, Ngan Nguyen, Duc Vu +11

Modern one-step diffusion models achieve impressive quality through distribution-based timestep distillation. Yet, they rely on a critical assumption: Teacher and Student must inha…

cs.CV2025

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…

cs.GR2025

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2022★ 33 cited

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…