As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Map** woody vegetation from aerial images is an important task bluein environment
monitoring and management. A few studies have shown that semantic segmentation …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

Abstract Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels
typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …

Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important
and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS …

The class activation maps are generated from the final convolutional layer of CNN. They can
highlight discriminative object regions for the class of interest. These discovered object …

As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging
task. Mainstream approaches follow a multi-stage framework and suffer from high training …

Extracting class activation maps (CAM) is arguably the most standard step of generating
pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the …

Learning semantic segmentation from weakly-labeled (eg, image tags only) data is
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …