Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval

Remote sensing technology has advanced rapidly in recent years. Because of the deployment of quantitative and qualitative sensors, as well as the evolution of powerful hardware and software platforms, it powers a wide range of civilian and military applications. This in turn leads to the availability of large data volumes suitable for a broad range of applications such as monitoring climate change. Yet, processing, retrieving, and mining large data are challenging. Usually, content-based remote sensing image (RS) retrieval approaches rely on a query image to retrieve relevant images from the dataset. To increase the flexibility of the retrieval experience, cross-modal representations based on text–image pairs are gaining popularity. Indeed, combining text and image domains is regarded as one of the next frontiers in RS image retrieval. Yet, aligning text to the content of RS images is particularly challenging due to the visual-sematic discrepancy between language and vision worlds. In this work, we propose different architectures based on vision and language transformers for text-to-image and image-to-text retrieval. Extensive experimental results on four different datasets, namely TextRS, Merced, Sydney, and RSICD datasets are reported and discussed

Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval

Remote sensing technology has advanced rapidly in recent years. Because of the deployment of quantitative and qualitative sensors, as well as the evolution of powerful hardware and software platforms, it powers a wide range of civilian and military applications. This in turn leads to the availability of large data volumes suitable for a broad range of applications such as monitoring climate change. Yet, processing, retrieving, and mining large data are challenging. Usually, content-based remote sensing image (RS) retrieval approaches rely on a query image to retrieve relevant images from the dataset. To increase the flexibility of the retrieval experience, cross-modal representations based on text–image pairs are gaining popularity. Indeed, combining text and image domains is regarded as one of the next frontiers in RS image retrieval. Yet, aligning text to the content of RS images is particularly challenging due to the visual-sematic discrepancy between language and vision worlds. In this work, we propose different architectures based on vision and language transformers for text-to-image and image-to-text retrieval. Extensive experimental results on four different datasets, namely TextRS, Merced, Sydney, and RSICD datasets are reported and discussed

Enabling Two-Way Communication of Deaf Using Saudi Sign Language

Disabled people are facing many difficulties communicating with others and involving in society. Modern societies have dedicated significant efforts to promote the integration of disabled individuals into their societies and services. Currently, smart healthcare systems are used to facilitate disabled people. The objective of this paper is to enable two-way communication of deaf individuals with the rest of society, thus enabling their migration from marginal elements of society to mainstream contributing elements. In the proposed system, we developed three modules; the sign recognition module (SRM) that recognizes the signs of a deaf individual, the speech recognition and synthesis module (SRSM) that processes the speech of a non-deaf individual and converts it to text, and an Avatar module (AM) to generate and perform the corresponding sign of the non-deaf speech, which were integrated into the sign translation companion system called Saudi deaf companion system (SDCS) to facilitate the communication from the deaf to the hearing and vice versa. This paper also contributes to the literature by utilizing our self-developed database, the largest Saudi Sign Language (SSL) database—the King Saud University Saudi-SSL (KSU-SSL). The proposed SDCS system performs 293 Saudi signs that are recommended by the Saudi Association for Hearing Impairment (SAHI) from 10 domains (healthcare, common, alphabets, verbs, pronouns and adverbs, numbers, days, kings, family, and regions)

Abstracts of 1st International Conference on Computational & Applied Physics

This book contains the abstracts of the papers presented at the International Conference on Computational & Applied Physics (ICCAP’2021) Organized by the Surfaces, Interfaces and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria, held on 26–28 September 2021. The Conference had a variety of Plenary Lectures, Oral sessions, and E-Poster Presentations. Conference Title: 1st International Conference on Computational & Applied PhysicsConference Acronym: ICCAP’2021Conference Date: 26–28 September 2021Conference Location: Online (Virtual Conference)Conference Organizer: Surfaces, Interfaces, and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria