Segmentation Model
Segmentation models aim to partition images into meaningful regions, a crucial task across diverse fields like medical imaging and autonomous driving. Current research emphasizes improving model robustness and efficiency, focusing on architectures like U-Nets, Transformers, and diffusion models, often incorporating techniques like continual learning and prompt engineering to adapt to new data or tasks with minimal retraining. These advancements are driving improvements in accuracy and reducing the need for extensive labeled datasets, leading to wider applicability in various scientific and industrial applications.
Papers
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
M.M.A. Valiuddin, R.J.G. van Sloun, C.G.A. Viviers, P.H.N. de With, F. van der Sommen
Weakly supervised image segmentation for defect-based grading of fresh produce
Manuel Knott, Divinefavour Odion, Sameer Sontakke, Anup Karwa, Thijs Defraeye
RadioActive: 3D Radiological Interactive Segmentation Benchmark
Constantin Ulrich, Tassilo Wald, Emily Tempus, Maximilian Rokuss, Paul F. Jaeger, Klaus Maier-Hein
MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data
Chika Maduabuchi, Ericmoore Jossou, Matteo Bucci