Abstract Recently, Meta AI Research approaches a general, promptable segment anything
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …

The recently proposed segment anything model (SAM) has made a significant influence in
many computer vision tasks. It is becoming a foundation step for many high-level tasks, like …

Weakly-supervised temporal action localization aims to localize and recognize actions in
untrimmed videos with only video-level category labels during training. Without instance …

With the explosive growth of videos, weakly-supervised temporal action localization (WS-
TAL) task has become a promising research direction in pattern analysis and machine …

Robust and reliable semantic segmentation in complex scenes is crucial for many real-life
applications such as autonomous safe driving and nighttime rescue. In most approaches, it …

Salient object detection (SOD) aims to identify standout elements in a scene, with recent
advancements primarily focused on integrating depth data (RGB-D) or temporal data from …

Due to the lack of temporal annotation, current Weakly-supervised Temporal Action
Localization (WTAL) methods are generally stuck into over-complete or incomplete …

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action
instances with only category labels. Most methods widely adopt the off-the-shelf …

Weakly-supervised temporal action localization (WTAL) is a practical yet challenging task.
Due to large-scale datasets, most existing methods use a network pretrained in other …

Weakly-supervised temporal action localization (WTAL) aims to detect action instances
given only video-level labels. To address the challenge, recent methods commonly employ …