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20182022
most citedA Two-Stage Approach to Few-Shot Learning for Image Recognition

156 citations · 156 across the 3 of their papers we have counts for

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cs.CV2025

LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation

Farzad Farhadzadeh, Debasmit Das, Shubhankar Borse +1

The rising popularity of large foundation models has led to a heightened demand for parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), which offer perform…

cs.CV2022

Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation

Shubhankar Borse, Hyojin Park, Hong Cai +3

This paper presents a novel framework to integrate both semantic and instance contexts for panoptic segmentation. In existing works, it is common to use a shared backbone to extrac…

cs.CV2020

Few-shot Image Recognition with Manifolds

Debasmit Das, J. H. Moon, C. S. George Lee

In this paper, we extend the traditional few-shot learning (FSL) problem to the situation when the source-domain data is not accessible but only high-level information in the form…

cs.CV2019

Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration

Debasmit Das, C. S. George Lee

Zero-shot learning (ZSL) for image classification focuses on recognizing novel categories that have no labeled data available for training. The learning is generally carried out wi…

cs.CV2018

Unsupervised Domain Adaptation using Regularized Hyper-graph Matching

Debasmit Das, C. S. George Lee

Domain adaptation (DA) addresses the real-world image classification problem of discrepancy between training (source) and testing (target) data distributions. We propose an unsuper…