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.

本文提出了一种基于多模态交互模块的小样本物体分割方法，该方法利用视觉和词嵌入的协同注意机制，使用图像级标签在 PASCAL-5i 数据集上实现了 4.8％ 的提高，并在 YouTube-VOS 数据集上展示了 TOSFL 实验的实例级和类别级结果。

使用视觉和语义嵌入的共同关注实现弱监督的少样本目标分割

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

Over the past few years, we have witnessed the success of deep learning in
image recognition thanks to the availability of large-scale human-annotated
datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have
covered a wide range of object categories, there are still a significant number
of objects that are not included. Can we perform the same task without a lot of
human annotations? In this paper, we are interested in few-shot object
segmentation where the number of annotated training examples are limited to 5
only. To evaluate and validate the performance of our approach, we have built a
few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes
with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our
dataset contains significant number of objects that have never been seen or
annotated in previous datasets, such as tiny daily objects, merchandise,
cartoon characters, logos, etc. We build our baseline model using standard
backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise,
we found that training our model from scratch using FSS-1000 achieves
comparable and even better results than training with weights pre-trained by
ImageNet which is more than 100 times larger than FSS-1000. Both our approach
and dataset are simple, effective, and easily extensible to learn segmentation
of new object classes given very few annotated training examples. Dataset is
available at this https URL

本文讨论了有限样本下的目标分割，并提出了可以通过少量的像素级别注释来训练模型的 FSS-1000 数据集以及使用标准骨干网络如 VGG-16，ResNet-101 和 Inception 进行训练该模型，结果表明其训练结果甚至比使用 ImageNet 进行预训练的模型更好。