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

arXiv: https://arxiv.org/abs/2001.09540 · HTML: https://mlsanity.com/paper/2001.09540/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2111.01418/) (2021)
- [Beyond Few-shot Object Detection: A Detailed Survey](https://mlsanity.com/paper/2408.14249/) (2024)
- [Query Semantic Reconstruction for Background in Few-Shot Segmentation](https://mlsanity.com/paper/2210.12055/) (2022)
- [CobNet: Cross Attention on Object and Background for Few-Shot Segmentation](https://mlsanity.com/paper/2210.11968/) (2022)

## Abstract (arXiv)

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.