5 papers · 1 filter
High-Frequency First: A Two-Stage Approach for Improving Image INR
Sumit Kumar Dam, Mrityunjoy Gain, Eui-Nam Huh +1
Implicit Neural Representations (INRs) have emerged as a powerful alternative to traditional pixel-based formats by modeling images as continuous functions over spatial coordinates…
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining
Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4
In this paper, we propose the FedFeat+ framework, which distinctively separates feature extraction from classification. We develop a two-tiered model training process: following lo…
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain +2
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Gener…
CCC++: Optimized Color Classified Colorization with Segment Anything Model (SAM) Empowered Object Selective Color Harmonization
Mrityunjoy Gain, Avi Deb Raha, Rameswar Debnath
In this paper, we formulate the colorization problem into a multinomial classification problem and then apply a weighted function to classes. We propose a set of formulas to transf…
CCC: Color Classified Colorization
Mrityunjoy Gain, Avi Deb Raha, Rameswar Debnath
Automatic colorization of gray images with objects of different colors and sizes is challenging due to inter- and intra-object color variation and the small area of the main object…