Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Inputs
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Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in a few segmentation settings, including pixel-level, scribbles and bounding boxes. These methods can be classified as "strongly labelled" support images because significant image editing efforts are required to provide the labeling. This paper takes another approach, i.e., only requiring image-level classification data for few-shot object segmentation. The large amount of image-level labelled data signifies this approach, if successful. The problem is challenging because there is no obvious features that can be used for segmentation in the image-level data. 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 requires only image-level labels for the first frame in order to segment objects in the following frames. TOSFL provides a novel benchmark for video segmentation, which can be used on a variety of public video data such as Youtube-VOS, as demonstrated in our experiment.
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