Scribble annotations have recently become popular in video salient object detection.
Previous methods only focus on utilizing shallow feature consistency for more integral …

Image-level weakly supervised semantic segmentation (WSSS) relies on class activation
maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative …

The current methods of auto-segmenting medical images are limited due to insufficient and
ambiguous pathonmorphological labeling. In clinical practice, rough classification labels …

Referring Expression Segmentation (RES), which is aimed at localizing and segmenting the
target according to the given language expression, has drawn increasing attention. Existing …

The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to
leverage a large amount of unlabeled or weakly labeled data. A common practice is to select …

Product surface defect detection is vital to ensure the quality, efficiency, and reliability of
industrial production. Deep-learning-based product surface defect inspection algorithms …

There are many types of retinal disease, and accurately detecting these diseases is crucial
for proper diagnosis. Convolutional neural networks (CNNs) typically perform well on …

We focus on tackling weakly supervised semantic segmentation with scribble-level
annotation. The regularized loss has been proven to be an effective solution for this task …

End-to-end weakly supervised semantic segmentation aims at optimizing a segmentation
model in a single-stage training process based on only image annotations. Existing methods …

In real-world scenarios, it is common that a video contains multiple actors and their activities.
Selectively localizing one specific actor and its action spatially and temporally via a …