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Significant progress has been made recently in developing few-shot objectsegmentation methods. Learning is shown to be successful in few-shotsegmentation settings, using pixel-level, scribbles and bounding boxsupervision. This paper takes another approach, i.e., only requiringimage-level label for few-shot object segmentation. We propose a novelmulti-modal interaction module for few-shot object segmentation that utilizes aco-attention mechanism using both visual and word embedding. Our model usingimage-level labels achieves 4.8% improvement over previously proposedimage-level few-shot object segmentation. It also outperforms state-of-the-artmethods that use weak bounding box supervision on PASCAL-5i. Our results showthat few-shot segmentation benefits from utilizing word embeddings, and that weare able to perform few-shot segmentation using stacked joint visual semanticprocessing with weak image-level labels. We further propose a novel setup,Temporal Object Segmentation for Few-shot Learning (TOSFL) for videos. TOSFLcan be used on a variety of public video data such as Youtube-VOS, asdemonstrated in both instance-level and category-level TOSFL experiments.

Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings