Weakly supervised zero-shot cross-modal semantic segmentation

This is the authors' abstract. We don't add key points for this paper.

Weakly supervised semantic segmentation reduces annotation costs by using less detailed data, such as image-level labels or bounding boxes, but often suffers from lower accuracy due to insufficient annotations, leading to classification and boundary errors. Additionally, training weakly-supervised models requires complex algorithms, increasing computational resources and training time. This paper introduces an algorithm that combines a generalization unsupervised segmentation model with zero-shot learning and cross-modal understanding between images and texts. This approach reduces training time and computational costs while improving object boundary recognition. Tested on the PASCAL VOC 2012 dataset, the algorithm achieves a mean Intersection over Union (MIoU) of 77.3% on the test set, with a minor increase in computational speed by only 0.04 seconds per frame.