1,721,131 research outputs found
Neonatal laparoscopic inguinal hernia repair: a 3-year experience
No full textTo retrospectively analyze the feasibility, safety and complication rate of laparoscopic inguinal herniorraphy in babies weighing 5 kg or less
Aspetti infrequenti della patologia dello stomaco operato: i fili di sutura pendenti nel lume anastomotico
No full text
GCK-Maps: A Scene Unbiased Representation for Efficient Human Action Recognition
No full textHuman action recognition from visual data is a popular topic in Computer Vision, applied in a wide range of domains. State-of-the-art solutions often include deep-learning approaches based on RGB videos and pre-computed optical flow maps. Recently, 3D Gray-Code Kernels projections have been assessed as an alternative way of representing motion, being able to efficiently capture space-time structures. In this work, we investigate the use of GCK pooling maps, which we called GCK-Maps, as input for addressing Human Action Recognition with CNNs. We provide an experimental comparison with RGB and optical flow in terms of accuracy, efficiency, and scene-bias dependency. Our results show that GCK-Maps generally represent a valuable alternative to optical flow and RGB frames, with a significant reduction of the computational burden
Food Image Classification: The Benefit of In-Domain Transfer Learning
No full textMonitoring food intake and calories may be fundamental for a healthy lifestyle and preventing nutrition-related illnesses. Recently, deep-learning approaches have been extensively exploited to provide an automatic analysis of food images. However, food image datasets have peculiar challenges, including fine granularity with a high intra-class and low inter-class variability. In this work, we focus on training strategies considering the typical scenario where data availability and computational resources are limited. Exploiting convolutional neural networks, we show that in-domain source datasets provide a better representation with respect to only using ImageNet, bringing a significant increase in test accuracy. We finally show that ensembling different CNN models further improves the learned representation
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