This paper introduces a novel optimized hybrid model combining Long Short-Term Memory (LSTM) and Transformer deep learning architectures designed for power load forecasting. It leverages the strengths ...

Multisource remote sensing images (RSIs) can capture the complementary information of ground objects for use in semantic segmentation. However, there can be inconsistency and interference noise among ...

Cryptographic techniques are reviewed in this literature review, with particular attention paid to their applicability, importance, contributions, and field strengths. These algorithms include DES, ...

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image ...

Restoring high-quality images from degraded hazy observations is a fundamental and essential task in the field of computer vision. While deep models have achieved significant success with synthetic ...

Human gaze provides valuable information on human focus and intentions, making it a crucial area of research. Recently, deep learning has revolutionized appearance-based gaze estimation. However, due ...

Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This ...

In this paper, we study the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present V2X-ViTs, a robust cooperative perception ...

Semantic segmentation of high-resolution remote sensing images is vital in downstream applications such as land-cover mapping, urban planning, and disaster assessment. Existing Transformer-based ...

True digital orthophoto maps (DOMs) are vital spatial data sources due to their high precision, detail, and accessibility. However, traditional generation methods using image differential correction ...

Image fusion facilitates the integration of information from various source images of the same scene into a composite image, thereby benefiting perception, analysis, and understanding. Recently, ...

Existing human visual perception-oriented image compression methods well maintain the perceptual quality of compressed images, but they may introduce fake details into the compressed images, and ...