2 citations · 3 across the 9 of their papers we have counts for
10 papers
TGSR: Text-Grounded Diffusion for Faithful Image Super-Resolution
Zhiming Hu, Ran Zhang, Angela Ye +7
Diffusion models have achieved remarkable success in image super-resolution (SR) by leveraging strong generative priors to enhance perceptual quality. However, for textual content,…
BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors
Dong Huo, Tristan Aumentado-Armstrong, Samrudhdhi B. Rangrej +8
Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) frames, relying on temporal r…
Augmenting Perceptual Super-Resolution via Image Quality Predictors
Fengjia Zhang, Samrudhdhi B. Rangrej, Tristan Aumentado-Armstrong +2
Super-resolution (SR), a classical inverse problem in computer vision, is inherently ill-posed, inducing a distribution of plausible solutions for every input. However, the desired…
GePSAn: Generative Procedure Step Anticipation in Cooking Videos
Mohamed Ashraf Abdelsalam, Samrudhdhi B. Rangrej, Isma Hadji +3
We study the problem of future step anticipation in procedural videos. Given a video of an ongoing procedural activity, we predict a plausible next procedure step described in rich…
GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction
Samrudhdhi B Rangrej, Kevin J Liang, Tal Hassner +1
Many online action prediction models observe complete frames to locate and attend to informative subregions in the frames called glimpses and recognize an ongoing action based on g…
Few-shot Learning with Noisy Labels
Kevin J Liang, Samrudhdhi B. Rangrej, Vladan Petrovic +1
Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: suppor…
Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes
Samrudhdhi B. Rangrej, Chetan L. Srinidhi, James J. Clark
Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses. However, in many appli…
A Probabilistic Hard Attention Model For Sequentially Observed Scenes
Samrudhdhi B. Rangrej, James J. Clark
A visual hard attention model actively selects and observes a sequence of subregions in an image to make a prediction. The majority of hard attention models determine the attention…
Visual Attention in Imaginative Agents
Samrudhdhi B. Rangrej, James J. Clark
We present a recurrent agent who perceives surroundings through a series of discrete fixations. At each timestep, the agent imagines a variety of plausible scenes consistent with t…
A deep learning framework for segmentation of retinal layers from OCT images
Karthik Gopinath, Samrudhdhi B Rangrej, Jayanthi Sivaswamy
Segmentation of retinal layers from Optical Coherence Tomography (OCT) volumes is a fundamental problem for any computer aided diagnostic algorithm development. This requires prepr…