Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

With the development of computer vision, the research on human activity understanding has
been greatly promoted. The recognition algorithm based on vision transformer has made …

The rapid advancements in pre-trained Large Language Models (LLMs) and Large
Multimodal Models (LMMs) have ushered in a new era of intelligent applications …

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great
promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction …

Temporal Graph Neural Networks are gaining popularity in modeling interactions on
dynamic graphs. Among them, Temporal Graph Attention Networks (TGAT) have gained …

Purpose Manual annotations for training deep learning models in auto-segmentation are
time-intensive. This study introduces a hybrid representation-enhanced sampling strategy …

Cloud computing has drastically changed the delivery and consumption of live streaming
content. The designs, challenges, and possible uses of cloud computing for live streaming …

Engine oil temperature is a key parameter for ensuring the optimal functioning of diesel
engines in locomotives. This paper proposes attention-enhanced CNN-LSTM based on …

Short-term customer load forecasting is vital for the normal operation of power systems.
Unfortunately, conventional machine learning-based forecasting methods are susceptible to …

Recognition of leaf diseases in agriculture is considered a significant aspect of ensuring
food quantity, quality, and production. In general, crop leaves are susceptible and fragile to …