Saliency guided self-attention network for weakly and semi-supervised semantic segmentation
Weakly supervised semantic segmentation (WSSS) using only image-level labels can
greatly reduce the annotation cost and therefore has attracted considerable research …
greatly reduce the annotation cost and therefore has attracted considerable research …
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
The rapid development of deep learning has made a great progress in image segmentation,
one of the fundamental tasks of computer vision. However, the current segmentation …
one of the fundamental tasks of computer vision. However, the current segmentation …
A multi-scale weakly supervised learning method with adaptive online noise correction for high-resolution change detection of built-up areas
Accurate change detection of built-up areas (BAs) fosters a comprehensive understanding of
urban development. The post-classification comparison (PCC) is a widely-used change …
urban development. The post-classification comparison (PCC) is a widely-used change …
Multi-granularity denoising and bidirectional alignment for weakly supervised semantic segmentation
Weakly supervised semantic segmentation (WSSS) models relying on class activation maps
(CAMs) have achieved desirable performance comparing to the non-CAMs-based …
(CAMs) have achieved desirable performance comparing to the non-CAMs-based …
ExplAIn: Explanatory artificial intelligence for diabetic retinopathy diagnosis
Abstract In recent years, Artificial Intelligence (AI) has proven its relevance for medical
decision support. However, the “black-box” nature of successful AI algorithms still holds back …
decision support. However, the “black-box” nature of successful AI algorithms still holds back …
Wave-like class activation map with representation fusion for weakly-supervised semantic segmentation
The Class Activation Map (CAM) is widely used to generate pseudo-labels for Weakly
Supervised Semantic Segmentation (WSSS), while it does not adequately consider the …
Supervised Semantic Segmentation (WSSS), while it does not adequately consider the …
Adaptive affinity loss and erroneous pseudo-label refinement for weakly supervised semantic segmentation
Semantic segmentation has been continuously investigated in the last ten years, and
majority of the established technologies are based on supervised models. In recent years …
majority of the established technologies are based on supervised models. In recent years …
Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language Models
From image-text pairs large-scale vision-language models (VLMs) learn to implicitly
associate image regions with words which prove effective for tasks like visual question …
associate image regions with words which prove effective for tasks like visual question …
Gaussian dynamic convolution for efficient single-image segmentation
Interactive single-image segmentation is ubiquitous in the scientific and commercial imaging
software. Lightweight neural network is one practical and effective way to accomplish the …
software. Lightweight neural network is one practical and effective way to accomplish the …
Weakly-supervised semantic segmentation with visual words learning and hybrid pooling
Weakly-supervised semantic segmentation (WSSS) methods with image-level labels
generally train a classification network to generate the Class Activation Maps (CAMs) as the …
generally train a classification network to generate the Class Activation Maps (CAMs) as the …