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 …
Multi-class token transformer for weakly supervised semantic segmentation
This paper proposes a new transformer-based framework to learn class-specific object
localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS) …
localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS) …
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 …
L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation
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 …
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
Causal intervention for weakly-supervised semantic segmentation
We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …
Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation
Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level
weak supervision have several limitations: sparse object coverage, inaccurate object …
weak supervision have several limitations: sparse object coverage, inaccurate object …
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 …
Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation
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 …
task. Mainstream approaches follow a multi-stage framework and suffer from high training …
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 …
Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation
Semantic segmentation is a challenging task in the absence of densely labelled data. Only
relying on class activation maps (CAM) with image-level labels provides deficient …
relying on class activation maps (CAM) with image-level labels provides deficient …