Zero Shot Segmentation
5 papers with code • 0 benchmarks • 0 datasets
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To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
Our model starts with two separate pathways: an appearance pathway that outputs feature-based region segmentation for a single image, and a motion pathway that outputs motion features for a pair of images.
Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and adapting the learned image-level understanding to the segmentation task.