AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Cross-domain Few-shot Segmentation (CD-FSS) aims to transfer knowledge learned from source domain to distinct target domains, segmenting unseen target classes with only a few annotated samples. Although existing methods have made significant progress, they still rely on training or fine-tuning processes, which incur high computational costs and risk overfitting.
arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).
arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.
The paper introduces UINO-FSS, a unified framework for few-shot semantic segmentation that merges knowledge from DINOv2 and SAM into a single-encoder architecture. It employs coarse-to-fine multimodal distillation, a bottleneck adapter for embedding alignment, a meta-visual prompt generator, and a mask decoder, enhanced by Mamba-based 4D correlation mining. Experiments on PASCAL-5i and COCO-20i demonstrate state‑of‑the‑art performance in 1‑shot settings, achieving 80.6% mIoU on PASCAL-5i and 64.5% on COCO-20i.
arXiv:2606. 09245v1 Announce Type: cross Abstract: Few-shot object detection has gained widely attention in recent years.
arXiv:2407.03463v2 Announce Type: replace-cross Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets fo...