Abstract: Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transformers) ...

Abstract: The Audio-Visual Question Answering (AVQA) task holds significant potential for applications. Compared to traditional unimodal approaches, the multi-modal input of AVQA makes feature ...

Abstract: Text-driven human motion generation has attracted considerable critical attention in recent years. The task requires generating movements that are diverse, natural, and comfortable in ...

Abstract: With the advancement of high-resolution aerial imaging technology enabled by unmanned aerial vehicles (UAVs), insulator defect detection based on images has emerged as a key approach for ...

Abstract: In biomedical image analysis, developing architectures that effectively capture long-range dependencies is crucial. Traditional Convolutional Neural Networks (CNNs) are constrained by their ...

Abstract: The low inertia of voltage estimation degrades system performance in islanded DC microgrids (DC MGs). To mitigate this issue, we propose an Adaptive Virtual Inertia and Voltage Estimation ...

Abstract: Transformers bring significantly improved performance to the light field image super-resolution task due to their long-range dependency modeling capability. However, the inherently high ...

Abstract: Chasing energy efficiency through biologically inspired computing has produced significant interest in neuromorphic computing as a new approach to overcome some of the limitations of ...

Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, the limited target priors constrain ...

Abstract: Hyperspectral anomaly detection (HAD) intends to detect potential anomalous targets hidden in the background of hyperspectral images (HSIs) and has garnered substantial attention in various ...