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 comprehensive survey on segment anything model for vision and beyond
Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the
ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence …
ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence …
Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models
Collecting and annotating images with pixel-wise labels is time-consuming and laborious. In
contrast, synthetic data can be freely available using a generative model (eg, DALL-E …
contrast, synthetic data can be freely available using a generative model (eg, DALL-E …
Token contrast for weakly-supervised semantic segmentation
Abstract Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels
typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …
typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …
Learning affinity from attention: End-to-end weakly-supervised semantic segmentation with transformers
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 …
and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS …
Regional semantic contrast and aggregation for weakly supervised semantic segmentation
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 …
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation
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 …
has attracted much attention due to low annotation costs. Existing methods often rely on …
Anti-adversarially manipulated attributions for weakly and semi-supervised semantic segmentation
Weakly supervised semantic segmentation produces a pixel-level localization from class
labels; but a classifier trained on such labels is likely to restrict its focus to a small …
labels; but a classifier trained on such labels is likely to restrict its focus to a small …
Reducing information bottleneck for weakly supervised semantic segmentation
Weakly supervised semantic segmentation produces pixel-level localization from class
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …
Physical attack on monocular depth estimation with optimal adversarial patches
Deep learning has substantially boosted the performance of Monocular Depth Estimation
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …