Current video anomaly detection (VAD) approaches with weak supervisions are inherently
limited to a closed-set setting and may struggle in open-world applications where there can …

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 …

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 …

Abstract Vision-Language Large Models (VLMs) recently become primary backbone of AI,
due to the impressive performance. However, their expensive computation costs, ie …

Zero-shot temporal action detection (ZS-TAD), aiming to recognize and detect new and
unseen video actions, is an emerging and challenging task with limited solutions. Recent …

As one of the fundamental video tasks in computer vision, Open-Vocabulary Action
Recognition (OVAR) recently gains increasing attention, with the development of vision …

Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the
impressive performance. However, their expensive computation costs, ie, throughput and …

In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-
training (CLIP) has made significant strides, becoming foundation for various downstream …

Spatio-temporal action localization aims to locate the spatial and temporal positions of
actors and classify their actions. However, prior research overlooks the fact that human …