We argue that a similarity-based support selection scheme can boost the performance of an FSS network. This scheme picks out top similar support images.
Upon reevaluating recent studies of Few-Shot Segmentation (FSS), a key observation is that the random selection of support images is not always the optimal.
Apr 17, 2024 · To this end, we propose a Siamese Support Selection Network (SSSN) which can be end-to-end trained along with an FSS network. We also leverage ...
Dec 11, 2023 · PDF | Upon reevaluating recent studies of Few-Shot Segmentation (FSS), a key observation is that the random selection of support images is ...
We introduce a simple, yet effective method to handle novel classes prediction in few-shot setting using learnable prompts.
Missing: scheme | Show results with:scheme
A Learnable Support Selection Scheme for Boosting Few-Shot Segmentation. Wenxuan Shao; Hao Qi; Xinghui Dong · INDTLab, Ocean University of China. Comparison of ...
This work uses predicted masks from FSS methods to generate prompts and then uses SAM to predict new masks to avoid predicting wrong masks with SAM, ...
Missing: learnable | Show results with:learnable
People also ask
What is few shot segmentation?
How do you train instance segmentation?
Jan 18, 2024 · We propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any ...
Missing: learnable scheme
Few-shot learning is a promising way for reducing the label cost in new categories adaptation with the guidance of a small, well labeled support set.
In this work, we revisit the prior mask guidance proposed in “Prior Guided Feature Enrichment Network for Few-Shot Segmentation”. The prior mask serves as ...