An Immuno-Inspired Transfer Learning Paradigm

Kulkarni, Divya D.; Nair, Shivashankar B.

doi:10.1109/cec45853.2021.9504697

Cited by 2 publications

(1 citation statement)

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“…Transfer learning is a paradigm to train on one task and transfer to a new task (Torrey and Shavlik 2010). Transfer learning consists of the transfer of knowledge from an old task to a newer task, which has some similarities with the old (Kulkarni and Nair 2021). Transfer and multi-task learning algorithms are not the same.…”

Section: A Comparison Between Transfer and Multi-task Learningmentioning

confidence: 99%

SA-NET.V2: Real-Time Vehicle Detection From Oblique Uav Images With Use of Uncertainty Estimation in Deep Meta-Learning

Khoshboresh-Masouleh

Shah-Hosseini

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In recent years, unmanned aerial vehicle (UAV) imaging is a suitable solution for real-time monitoring different vehicles on the urban scale. Real-time vehicle detection with the use of uncertainty estimation in deep meta-learning for the portable platforms (e.g., UAV) potentially improves video understanding in real-world applications with a small training dataset, while many vehicle monitoring approaches appear to understand single-time detection with a big training dataset. The purpose of real-time vehicle detection from oblique UAV images is to locate the vehicle on the time series UAV images by using semantic segmentation. Real-time vehicle detection is more difficult due to the variety of depth and scale vehicles in oblique view UAV images. Motivated by these facts, in this manuscript, we consider the problem of real-time vehicle detection for oblique UAV images based on a small training dataset and deep meta-learning. The proposed architecture, called SA-Net.v2, is a developed method based on the SA-CNN for real-time vehicle detection by reformulating the squeeze-and-attention mechanism. The SA-Net.v2 is composed of two components, including the squeeze-and-attention function that extracts the high-level feature based on a small training dataset, and the gated CNN. For the real-time vehicle detection scenario, we test our model on the UAVid dataset. UAVid is a time series oblique UAV images dataset consisting of 30 video sequences. We examine the proposed method's applicability for stand real-time vehicle detection in urban environments using time series UAV images. The experiments show that the SA-Net.v2 achieves promising performance in time series oblique UAV images.

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Section: A Comparison Between Transfer and Multi-task Learningmentioning

confidence: 99%