Data augmentation for object detection: A review
P Kaur, BS Khehra, EBS Mavi - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Deep learning has been a game changer in the field of object detection in the last decade.
But all the deep learning models for computer vision depend upon large amount of data for …
But all the deep learning models for computer vision depend upon large amount of data for …
LSNet: Lightweight spatial boosting network for detecting salient objects in RGB-thermal images
Most recent methods for RGB (red–green–blue)-thermal salient object detection (SOD)
involve several floating-point operations and have numerous parameters, resulting in slow …
involve several floating-point operations and have numerous parameters, resulting in slow …
xViTCOS: explainable vision transformer based COVID-19 screening using radiography
Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19)
across the globe has pushed the health care system in many countries to the verge of …
across the globe has pushed the health care system in many countries to the verge of …
Beyond automatic medical image segmentation—the spectrum between fully manual and fully automatic delineation
Semi-automatic and fully automatic contouring tools have emerged as an alternative to fully
manual segmentation to reduce time spent contouring and to increase contour quality and …
manual segmentation to reduce time spent contouring and to increase contour quality and …
There is more than meets the eye: Self-supervised multi-object detection and tracking with sound by distilling multimodal knowledge
FR Valverde, JV Hurtado… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Attributes of sound inherent to objects can provide valuable cues to learn rich
representations for object detection and tracking. Furthermore, the co-occurrence of …
representations for object detection and tracking. Furthermore, the co-occurrence of …
Relevance-cam: Your model already knows where to look
With increasing fields of application for neural networks and the development of neural
networks, the ability to explain deep learning models is also becoming increasingly …
networks, the ability to explain deep learning models is also becoming increasingly …
Feature map distillation of thin nets for low-resolution object recognition
Z Huang, S Yang, MC Zhou, Z Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Intelligent video surveillance is an important computer vision application in natural
environments. Since detected objects under surveillance are usually low-resolution and …
environments. Since detected objects under surveillance are usually low-resolution and …
Cross-architecture knowledge distillation
Transformer attracts much attention because of its ability to learn global relations and
superior performance. In order to achieve higher performance, it is natural to distill …
superior performance. In order to achieve higher performance, it is natural to distill …
Transfer without forgetting
This work investigates the entanglement between Continual Learning (CL) and Transfer
Learning (TL). In particular, we shed light on the widespread application of network …
Learning (TL). In particular, we shed light on the widespread application of network …
Pulmonary COVID-19: learning spatiotemporal features combining CNN and LSTM networks for lung ultrasound video classification
B Barros, P Lacerda, C Albuquerque, A Conci - Sensors, 2021 - mdpi.com
Deep Learning is a very active and important area for building Computer-Aided Diagnosis
(CAD) applications. This work aims to present a hybrid model to classify lung ultrasound …
(CAD) applications. This work aims to present a hybrid model to classify lung ultrasound …