Small sample instance segmentation is a very challenging task, and many existing methods follow the training strategy of meta-learning which pre-train models on support set and fine-tune on query set. The pre-training phase, which is highly task related, requires a significant amount of additional training time and the selection of datasets with close proximity to ensure effectiveness. The article proposes a novel small sample instance segmentation solution from the perspective of maximizing the utilization of existing information without increasing annotation burden and training costs. The proposed method designs two modules to address the problems encountered in small sample instance segmentation. First, it helps the model fully utilize unlabeled data by learning to generate pseudo labels, increasing the number of available samples. Second, by integrating the features of text and image, more accurate classification results can be obtained. These two modules are suitable for box-free and box-dependent frameworks. In the way, the proposed method not only improves the performance of small sample instance segmentation, but also greatly reduce reliance on pre-training. We have conducted experiments in three datasets from different scenes: on land, underwater and under microscope. As evidenced by our experiments, integrated image-text corrects the confidence of classification, and pseudo labels help the model obtain preciser masks. All the results demonstrate the effectiveness and superiority of our method.

翻译：小样本实例分割是一项极具挑战性的任务，现有方法多遵循元学习的训练策略，即在支持集上预训练模型并在查询集上进行微调。与任务高度相关的预训练阶段需要大量额外训练时间，且需选择高度接近的数据集以保证有效性。本文提出一种新颖的小样本实例分割解决方案，其核心在于不增加标注负担与训练成本的前提下，最大化利用现有信息。所提方法设计了两个模块以应对小样本实例分割中遇到的问题：首先，通过学习生成伪标签帮助模型充分利用未标注数据，从而增加可用样本数量；其次，通过融合文本与图像特征，可获得更精确的分类结果。这两个模块适用于无框依赖与有框依赖的框架。该方法不仅提升了小样本实例分割的性能，同时显著降低了对预训练的依赖。我们在陆地、水下及显微场景的三个数据集上进行了实验验证。实验结果表明，图像文本集成有效修正了分类置信度，伪标签则帮助模型获得更精确的掩码。所有结果均证明了本方法的有效性与优越性。