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