12 citations · 32 across the 8 of their papers we have counts for
11 papers
Listen, Look, and Learn: Learning Without Forgetting through SAM-Audio
Avi Gupta, Nilotpal Sinha, Vishnu Raj +4
Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. While recent CIL advances have spurred significant interes…
SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model
Saurabh Yadav, Avi Gupta, Koteswar Rao Jerripothula
The emergence of large foundation models has propelled significant advances in various domains. The Segment Anything Model (SAM), a leading model for image segmentation, exemplifie…
Federated Learning for Commercial Image Sources
Shreyansh Jain, Koteswar Rao Jerripothula
Federated Learning is a collaborative machine learning paradigm that enables multiple clients to learn a global model without exposing their data to each other. Consequently, it pr…
SymFace: Additional Facial Symmetry Loss for Deep Face Recognition
Pritesh Prakash, Koteswar Rao Jerripothula, Ashish Jacob Sam +2
Over the past decade, there has been a steady advancement in enhancing face recognition algorithms leveraging advanced machine learning methods. The role of the loss function is pi…
Comprehensive Saliency Fusion for Object Co-segmentation
Harshit Singh Chhabra, Koteswar Rao Jerripothula
Object co-segmentation has drawn significant attention in recent years, thanks to its clarity on the expected foreground, the shared object in a group of images. Saliency fusion ha…
ASOC: Adaptive Self-aware Object Co-localization
Koteswar Rao Jerripothula, Prerana Mukherjee
The primary goal of this paper is to localize objects in a group of semantically similar images jointly, also known as the object co-localization problem. Most related existing wor…