Aug 22, 2023 · We propose a joint learning method termed Masked Cross-Image Encoding (MCE), which is designed to capture common visual properties that describe object details.

We propose a joint learning method termed Masked Cross-Image Encoding (MCE), which is designed to capture common visual properties that describe object details.

Abstract—Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes.

Sep 12, 2024 · Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited ...

Deep learning models have made remarkable strides in semantic segmentation tasks by training on extensive datasets with rich annotations.

In general, semantic segmentation systems consist of three parts: an encoder, a decoder, and a classifier. To integrate meta-learning, existing models typically ...

Few-shot semantic segmentation (FSS) learns to segment target objects in query image given few pixel-wise annotated support image.

Masked Cross-image Encoding for Few-shot Segmentation. 2023. ResNet-50. 38. VAT (HM, ResNet-50). 65.8, 77.1. HM: Hybrid Masking for Few-Shot Segmentation. 2022.

Jul 16, 2024 · Existing few-shot segmentation (FSS) methods mainly focus on prototype feature generation and the query-support matching mechanism.

We introduce the masked cross attention mechanism that only attends agent tokens with support pixels restricted to the foreground region. In this way, agent ...