This paper discusses the creation of a database of 3D mesh models of real-world objects, vital for robotic simulations and applications like computer vision and AI. Traditional CAD-based methods are…
Continue readingThe article discusses the challenge of 6D object pose estimation and the improved accuracy achieved by incorporating stereo vision. It highlights the creation of a BOP compatible stereo version of…
Continue readingThe article presents a new approach to dealing with computational limitations in developing computer vision solutions for gigapixel images in digital pathology. The authors propose a method of knowledge distillation…
Continue readingThis paper presents the Global Feature Pyramid Network (GFPNet), an improved version of PAFPN that uses global data to enhance target detection. The GFPNet addresses the issue of increased false…
Continue readingThe work involves the use of computer vision to enhance the accuracy of lighting elements detection in buildings. The aim is to improve the precision of the inventory of the…
Continue readingThis research proposes a novel solution for medical image classification using Graph Neural Networks (GNNs). The approach uses Image Foresting Transform to segment images, which are then transformed into graph-structured…
Continue readingThe study proposes a method for evaluating the significance of roadside billboards through videos captured from a driver’s perspective. A new dataset, BillboardLamac, was collected and annotated, consisting of eight…
Continue readingThis research paper demonstrates the potential of using paint markings for automatic honey bee behavior analysis in the field. A novel dataset for bee re-identification with paint markings is contributed,…
Continue readingThe paper by Oliver Turnbull and George Cevora from Artefact Ltd. discusses the inherent instability of computer vision models. The authors argue that adversarial examples, which are small changes to…
Continue readingThis paper proposes a strategy for eczema segmentation using a model called SegGPT and visual in-context learning. The method requires fewer examples and no retraining models, making it more efficient…
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