Image-based visual-language (I-VL) pre-training has shown great success for learning joint
visual-textual representations from large-scale web data, revealing remarkable ability for …

Temporal sentence grounding aims to detect the most salient moment corresponding to the
natural language query from untrimmed videos. As labeling the temporal boundaries is labor …

Open-vocabulary semantic segmentation is a challenging task that requires segmenting
novel object categories at inference time. Recent works explore vision-language pre-training …

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 …

In temporal action segmentation, Timestamp Supervision requires only a handful of labelled
frames per video sequence. For unlabelled frames, previous works rely on assigning hard …

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 …

Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames.
Although great progress has been witnessed we experimentally reveal that current methods …

Detecting temporal actions in a video with only single-frame annotation in each action
instance or segment, aka, point-level supervision, has emerged as a more challenging task …

In this paper, we consider the problem of temporal action localization under low-shot (zero-
shot & few-shot) scenario, with the goal of detecting and classifying the action instances from …

Point-level weakly-supervised temporal action localization (P-WSTAL) aims to localize
temporal extents of action instances and identify the corresponding categories with only a …