274 citations · 617 across the 33 of their papers we have counts for
12 papers · 2 filters
Generalization vs. Specialization: Evaluating Segment Anything Model (SAM3) Zero-Shot Segmentation Against Fine-Tuned YOLO Detectors
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee +1
Deep learning has advanced two fundamentally different paradigms for instance segmentation: specialized models optimized through task-specific fine-tuning and generalist foundation…
The SAM2-to-SAM3 Gap in the Segment Anything Model Family: Why Prompt-Based Expertise Fails in Concept-Driven Image Segmentation
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This paper investigates the fundamental discontinuity between the latest two Segment Anything Models: SAM2 and SAM3. We explain why the expertise in prompt-based segmentation of SA…
Ultralytics YOLO Evolution: An Overview of YOLO27, YOLO26, YOLO11, YOLOv8, and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition
Ranjan Sapkota, Manoj Karkee
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 thro…
Object Detection with Multimodal Large Vision-Language Models: An In-depth Review
Ranjan Sapkota, Manoj Karkee
The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, a…
YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection
Ranjan Sapkota, Rahul Harsha Cheppally, Ajay Sharda +1
This study presents a comprehensive analysis of Ultralytics YOLO26(also called as YOLOv26), highlighting its key architectural enhancements and performance benchmarking for real-ti…
3D Reconstruction and Information Fusion between Dormant and Canopy Seasons in Commercial Orchards Using Deep Learning and Fast GICP
Ranjan Sapkota, Zhichao Meng, Martin Churuvija +3
In orchard automation, dense foliage during the canopy season severely occludes tree structures, minimizing visibility to various canopy parts such as trunks and branches, which li…
Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges
Ranjan Sapkota, Yang Cao, Konstantinos I. Roumeliotis +1
Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action wi…
Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks
Konstantinos I. Roumeliotis, Ranjan Sapkota, Manoj Karkee +2
Automation in agriculture plays a vital role in addressing challenges related to crop monitoring and disease management, particularly through early detection systems. This study in…
A Review of 3D Object Detection with Vision-Language Models
Ranjan Sapkota, Konstantinos I Roumeliotis, Rahul Harsha Cheppally +2
This review provides a systematic analysis of comprehensive survey of 3D object detection with vision-language models(VLMs) , a rapidly advancing area at the intersection of 3D vis…
RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity
Ranjan Sapkota, Rahul Harsha Cheppally, Ajay Sharda +1
This study conducts a detailed comparison of RF-DETR object detection base model and YOLOv12 object detection model configurations for detecting greenfruits in a complex orchard en…
Improved YOLOv12 with LLM-Generated Synthetic Data for Enhanced Apple Detection and Benchmarking Against YOLOv11 and YOLOv10
Ranjan Sapkota, Manoj Karkee
This study evaluated the performance of the YOLOv12 object detection model, and compared against the performances YOLOv11 and YOLOv10 for apple detection in commercial orchards bas…
Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey
Ranjan Sapkota, Shaina Raza, Maged Shoman +2
In the past five years, research has shifted from traditional Machine Learning (ML) and Deep Learning (DL) approaches to leveraging Large Language Models (LLMs) , including multimo…