Emotion Attention-Aware Collaborative Deep Reinforcement Learning for Image Cropping

Zhang, Xiaoyan; Li, Zhuopeng; Jiang, Jianmin

doi:10.1109/tmm.2020.3013350

Cited by 19 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make the processed image more visually appealing, IAP entails the automatic correction or enhancement of an image's aesthetic content. For example, Zhang et al [25] proposed a collaborative deep reinforcement learning model for automatic image cropping. Wang et al [26] designed a two-branch neural network for attention box prediction and aesthetic assessments.…”

Section: Image Aesthetic Processingmentioning

confidence: 99%

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Zhao,

Shi,

Han

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Image aesthetics processing (IAP) is used primarily to enhance the aesthetic quality of images. However, IAP faces several issues, including its failure to analyze the influence of visual scene information and the difficulty of deploying IAP capabilities to mobile devices. This study proposes an automatic IAP system (IAPS) for mobile devices that integrates machine learning and traditional image-processing methods. First, we employ an extremely computation-efficient deep learning model, ShuffleNet, designed for mobile devices as our scene recognition model. Then, to enable computational inferencing on resource-constrained edge devices, we use a modern mobile machine-learning library, TensorFlow Lite, to convert the model type to TFLite format. Subsequently, we adjust the image contrast and color saturation using group filtering, respectively. These methods enable us to achieve maximal aesthetic enhancement of images with minimal parameter adjustments. Finally, we use the InceptionResNet-v2 aesthetic evaluation model to rate the images. Even when employing the benchmark model with an accuracy of 70%, the score of the IAPS processing image is verified to be higher and more effective compared with a state-of-the-art smartphone’s beautification function. Additionally, an anonymous questionnaire survey with 100 participants is conducted, and the result shows that IAPS enhances the aesthetic appeal of images based on the public’s preferences.

show abstract

Section: Image Aesthetic Processingmentioning

confidence: 99%

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Zhao,

Shi,

Han

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…It is a generic system that isn't programmed particularly to solve a problem, but instead improves and learns from experience to solve a problem. [5]ML algorithms don't rely on predetermined calculations as a structure, instead they "learn" information by using computational methods [6]. As the number of learning samples increases, the algorithms enhance their performances.…”

Section: Machine Learningmentioning

confidence: 99%

Automatic Portrait Image Cropping using Machine Learning Models

Rani¹,

Sruthi²

2023

IJSMEM

View full text Add to dashboard Cite

For a long time, machine learning is an application spanning from a wide variety of subjects – from vehicles to data extraction. When you take an image of your cell phone, the picture is a little tangy. It’s simple. It often happens that people take random pictures using phones, which may end up in a corner of the frame. This work blends computer study with tools for photo editing. It will explore the options of how to automatically create photos with aesthetic pleasure through machine learning and how to create a portrait cutting tool. It also explores how to use machine learning to incorporate a streamlined function. Finally, the tools will be compared to other automated machine cropping tools.

show abstract

“…Li et al propose an image cropping model based on weakly-supervised DRL in [29]. In the same way, Zhang et al construct two agents to move and zoom the bounding-box with the emotional and background information in the image [30]. By simulating an agent as a brush, Zhang et al sequentially relight portraits by DRL and learned a coarse-to-fine policy with local interpretability [31].…”

Section: B Computer Vision With Deep Reinforcement Learningmentioning

confidence: 99%

Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems

Xu¹,

Song²,

Guo³

et al. 2021

Preprint

View full text Add to dashboard Cite

The vigorous developments of Internet of Things make it possible to extend its computing and storage capabilities to computing tasks in the aerial system with collaboration of cloud and edge, especially for artificial intelligence (AI) tasks based on deep learning (DL). Collecting a large amount of image/video data, Unmanned aerial vehicles (UAVs) can only handover intelligent analysis tasks to the back-end mobile edge computing (MEC) server due to their limited storage and computing capabilities. How to efficiently transmit the most correlated information for the AI model is a challenging topic. Inspired by the task-oriented communication in recent years, we propose a new aerial image transmission paradigm for the scene classification task. A lightweight model is developed on the frontend UAV for semantic blocks transmission with perception of images and channel conditions. In order to achieve the tradeoff between transmission latency and classification accuracy, deep reinforcement learning (DRL) is used to explore the semantic blocks which have the best contribution to the back-end classifier under various channel conditions. Experimental results show that the proposed method can significantly improve classification accuracy compared to the fixed transmission strategy and traditional content perception methods.

show abstract

Emotion Attention-Aware Collaborative Deep Reinforcement Learning for Image Cropping

Cited by 19 publications

References 35 publications

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Automatic Portrait Image Cropping using Machine Learning Models

Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems

Contact Info

Product

Resources

About