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 …

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

Single-frame temporal action localization (STAL) aims to localize actions in untrimmed
videos with only one timestamp annotation for each action instance. Existing methods adopt …

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 …

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 …

The temporal action localization research aims to discover action instances from untrimmed
videos, representing a fundamental step in the field of intelligent video understanding. With …

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 …

In semantic segmentation, adapting a visual system to novel object categories at inference
time has always been both valuable and challenging. To enable such generalization …

Temporal sentence grounding aims to detect the event timestamps described by the natural
language query from given untrimmed videos. The existing fully-supervised setting achieves …