Alignsam: Aligning segment anything model to open context via reinforcement learning

D Huang, X Xiong, J Ma, J Li, Z Jie… - Proceedings of the …, 2024 - openaccess.thecvf.com
Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …

Reinforcement Learning in Image Classification: A Review

N Alrebdi, S Alrumiah, A Almansour… - 2022 2nd International …, 2022 - ieeexplore.ieee.org
Image classification experiments face several problems related to image specifications, size
of samples, and classification accuracy. The image classification related issues motivated …

MT-RAM: Multi Task-Recurrent Attention Model for partially observable image anomaly classification and localization

J Guo, C Han, Y Ma, C Zhang - IISE Transactions, 2024 - Taylor & Francis
With the rapid development of the digital manufacturing industry, the nature of quality data
has transformed from simple univariate or multivariate characteristics to big data comprising …

DRSAL: Deep Reinforcement Skin Cancer Diagnosis with Active Learning Technique

G Renith, A Senthilselvi - 2022 Third International Conference …, 2022 - ieeexplore.ieee.org
Malignancy is the most dangerous disease that causes higher fatality rate among humans.
Albeit, different malignancy found in recent times, skin cancer is considered as highly …

DRA-ODM: a faster and more accurate deep recurrent attention dynamic model for object detection

G Li, F Xu, H Li, Y Yuan, M An - World Wide Web, 2022 - Springer
The traditional object detection model based on convolutional neural network contains a
large amount of parameters, so it has poor performance when applied to high-real-time and …

로봇물체조작작업을위한동적3 차원장면그래프생성

정가영, 김인철 - 제어로봇시스템학회논문지, 2021 - dbpia.co.kr
In this study, we proposed a novel dataset and a deep learning model that can generate
three-dimensional (3D) dynamic scene graphs for robotic manipulation tasks. First, we …