This research presents a new annotation strategy, Drag&Drop, for tumor detection and localization. This strategy simplifies the annotation process making it more efficient, particularly for temporal and volumetric imaging. The…
Continue readingThis research focuses on the creation of a new set of multimodal remote sensing benchmark datasets for cross-city semantic segmentation tasks. The dataset, called the C2Seg, includes two cross-city scenes,…
Continue readingThe research by Ben Lonnqvist, Zhengqing Wu, and Michael H. Herzog focuses on the role of neural noise in perceptual grouping and segmentation in deep neural networks (DNNs). They argue…
Continue readingThe study presents a counterintuitive approach to unsupervised perceptual grouping and segmentation in Deep Neural Networks (DNNs). It proposes that these arise due to neural noise, instead of in spite…
Continue readingThe paper presents an end-to-end trainable network for segmenting farmlands with contour levees from high-resolution aerial imagery. The network uses a fusion block that includes multiple voting blocks for image…
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…
Continue readingThe paper introdces a novel method called PanoOcc, which aims to provide a comprehensive model of the surrounding 3D world, a key requirement for autonomous driving. Existing perception tasks like…
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