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Feature enhanced cascading attention network for lightweight image super-resolution.
Sci Rep
January 2025
Zhongyu (Fujian) Digital Technology Co., Ltd, Fuzhou, 350108, China.
Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA).
View Article and Find Full Text PDFA framework for assessing reliability of observer annotations of aerial wildlife imagery, with insights for deep learning applications.
PLoS One
January 2025
Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America.
There is growing interest in using deep learning models to automate wildlife detection in aerial imaging surveys to increase efficiency, but human-generated annotations remain necessary for model training. However, even skilled observers may diverge in interpreting aerial imagery of complex environments, which may result in downstream instability of models. In this study, we present a framework for assessing annotation reliability by calculating agreement metrics for individual observers against an aggregated set of annotations generated by clustering multiple observers' observations and selecting the mode classification.
View Article and Find Full Text PDFShaderNN: A Lightweight and Efficient Inference Engine for Real-time Applications on Mobile GPUs.
Neurocomputing (Amst)
January 2025
Department of Electrical and Computer Engineering, University of Maryland at College Park, 8223 Paint Branch Dr, College Park, MD, 20740, USA.
Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited computational capacity of the devices, low power budget, varied memory access methods, and I/O bus bandwidth governed by the underlying processor's architecture. Furthermore, integrating an inference framework with time-sensitive applications - such as games and video-based software to perform tasks like ray tracing denoising and video processing - introduces the need to minimize data movement between processors and increase data locality in the target processor.
View Article and Find Full Text PDFSecondary intact capsulectomy with seroma without implant: revision of an incomplete treatment of BIA-ALCL - a case report.
Case Reports Plast Surg Hand Surg
January 2025
Department of Neuroscience, Mental Health and Sense Organs (NESMOS), Sant' Andrea Hospital, Rome, Italy.
Background: Breast Implant Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) is a haematological malignancy which may occur in patients with textured breast implant history. While typically diagnosed at an early stage with good prognosis, it may present with local residual disease due to incomplete surgical excision.
Case Presentation: We describe the case of a 42 year-old woman with a history of bilateral breast augmentation for cosmetic purposes 21 years prior, who developed recurring seroma of the left side.
Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise.
PLoS One
January 2025
Department of Information Technology, Politeknik Negeri Padang, Padang, Sumatera Barat, Indonesia.
Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier.
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