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IEEE23h
A Hybrid LSTM-Transformer Model for Power Load Forecasting
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 ...
IEEE23h
CFFormer: A Cross-Fusion Transformer Framework for the Semantic Segmentation of Multisource Remote Sensing Images - IEEE Xplore
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 ...
IEEE1d
Disentangling Before Composing: Learning Invariant Disentangled Features for Compositional Zero-Shot Learning | IEEE Journals & Magazine | IEEE Xplore
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 ...
IEEE3d
Advancing Real-World Image Dehazing: Perspective, Modules, and Training - IEEE Xplore
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 ...
IEEE2d
Appearance-Based Gaze Estimation With Deep Learning: A Review and Benchmark - IEEE Xplore
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 ...
IEEE2d
V2X-ViTv2: Improved Vision Transformers for Vehicle-to-Everything Cooperative Perception - IEEE Xplore
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 ...
IEEE1d
Integrating LLMs With ITS: Recent Advances, Potentials, Challenges, and Future Directions - IEEE Xplore
Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This ...
IEEE1d
A Comprehensive Analysis of Cryptographic Algorithms: Evaluating Security, Efficiency, and Future Challenges - IEEE Xplore
Cryptographic techniques are reviewed in this literature review, with particular attention paid to their applicability, importance, contributions, and field strengths. These algorithms include DES, ...
IEEE6d
UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images - IEEE Xplore
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 ...
IEEE2d
Ortho-3DGS: True Digital Orthophoto Generation From Unmanned Aerial Vehicle Imagery Using the Depth-Regulated 3D Gaussian Splatting - IEEE Xplore
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 ...
IEEE5d
High-Density Surface EMG Decomposition: Achievements, Challenges, and Concerns - IEEE Xplore
High-density surface electromyography (EMG) decomposition provides a valuable non-invasive approach to accessing key motor unit information for a range of applications. This communication summarizes ...
IEEE3d
JND-LIC: Learned Image Compression via Just Noticeable Difference for Human Visual Perception - IEEE Xplore
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 ...