DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection

Asali, Ehsan; Shenavarmasouleh, Farzan; Mohammadi, Farid Ghareh; Suresh, Prasanth Sengadu; Arabnia, Hamid R.

doi:10.48550/arxiv.2007.06809

Cited by 4 publications

(4 citation statements)

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“…In this study, we aim to extend and customize this architecture to make it yield the severity of the damage caused by diabetic retinopathy to the eye in addition to the previously mentioned info. Following our work in [15] we hypothesize that this can be achieved by designing a new pipeline and expand the underlying main model. The new model will also have the benefit of being able to be trained in an end-to-end manner.…”

Section: Proposed Methods and Problem Descriptionmentioning

confidence: 99%

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Shenavarmasouleh,

Mohammadi,

Amini

et al. 2021

Preprint

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Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical imaging. In this research, we propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find not only the type of lesions caused by DR, their corresponding bounding boxes, and their masks; but also the severity level of the overall case. Aside from traditional accuracy, we also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP). We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.

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Section: Proposed Methods and Problem Descriptionmentioning

confidence: 99%

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Shenavarmasouleh,

Mohammadi,

Amini

et al. 2021

Preprint

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“…In addition to the above methods, [12] considers the hybrid-based approach that benefits the combination of instance-based and parameterbased algorithms. On the other hand, deep networks have recently gained much attention in the machine learning community and have been applied in many applications such as Natural Language Processing [13], image analysis [14], [15], video analysis [16], speech recognition [17], [18], and many others. The growth of available annotated data and the success of deep learning in discovering the underlying structure of mass data in the past decade led the researcher to introduce a new methodology known as deep transfer learning.…”

Section: Approachmentioning

confidence: 99%

A Concise Review of Transfer Learning

Farahani¹,

Pourshojae²,

Rasheed³

et al. 2021

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The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data. Transfer learning aims to boost the performance of a target learner by applying another related source data. In contrast to the traditional machine learning and data mining techniques, which assume that the training and testing data lie from the same feature space and distribution, transfer learning can handle situations where there is a discrepancy between domains and distributions. These characteristics give the model the potential to utilize the available related source data and extend the underlying knowledge to the target task achieving better performance. This survey paper aims to give a concise review of traditional and current transfer learning settings, existing challenges, and related approaches.

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“…Effective collaboration/cooperation requires a robust, accurate, efficient and scalable coordination mechanism and, such coordination among agents, requires communications, which is constrained by many limiting factors. As a solution to all of the mentioned coordination and communication problems, RL, specifically when combined with deep learning (Deep RL 5 ) and function approximation, has been widely studied in recent years [11,12,13,14,15,16,17,18,83]. These frameworks intend to enable the agents in a team to "learn how to cooperate".…”

Section: Learning To Cooperate: Coordination and Communication Learningmentioning

confidence: 99%

FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot Teams

Seraj¹,

Wu²,

Gombolay³

2020

Preprint

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The purpose of this tutorial is to help individuals use the FireCommander game environment for research applications. The FireCommander is an interactive, probabilistic joint perceptionaction reconnaissance environment in which a composite team of agents (e.g., robots) cooperate to fight dynamic, propagating firespots (e.g., targets). In FireCommander game, a team of agents must be tasked to optimally deal with a wildfire situation in an environment with propagating fire areas and some facilities such as houses, hospitals, power stations, etc. The team of agents can accomplish their mission by first sensing (e.g., estimating fire states), communicating the sensed fire-information among each other and then taking action to put the firespots out based on the sensed information (e.g., dropping water on estimated fire locations). The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming. There are four important facets of the FireCommander environment that overall, create a non-trivial game:1.

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DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection

Cited by 4 publications

References 0 publications

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

A Concise Review of Transfer Learning

FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot Teams

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