For example, STFT improves the still image baseline FCOS by 10. 6% and 20. 6% on the comprehensive F1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets, respectively, and outperforms the state-of-the-art video-based method by 3. 6% and 8. 0%, respectively.
The recent vision transformer(i. e. for image classification) learns non-local attentive interaction of different patch tokens.
This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network.
To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation.
A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner.
Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.