As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model …
We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of se-mantic …
This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS) …
L Ru, Y Zhan, B Yu, B Du - … of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
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 …
L Ru, H Zheng, Y Zhan, B Du - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Abstract Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …
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 …
T Zhou, M Zhang, F Zhao, J Li - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
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 …
Mining precise class-aware attention maps, aka, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
Abstract Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has attracted much attention due to low annotation costs. Existing methods often rely on …