Video moment retrieval aims to locate the timestamps best matching the query description
within an untrimmed video. However, existing video moment retrieval approaches typically …

Recently, temporal action localization (TAL) methods, especially the weakly-supervised and
unsupervised ones, have become a hot research topic. Existing unsupervised methods …

When data labels are scarce, contrastive learning is often used to learn representations in a
weakly-supervised or unsupervised way. In contrastive learning, not only the learning …

Weakly supervised temporal action localization aims to locate the temporal boundaries of
action instances in untrimmed videos using video-level labels and assign them the …

Weakly supervised temporal action localization aims to localize action instances with only
video-level supervision. Due to the absence of frame-level annotation supervision, how …

Weakly-supervised temporal action localization (WTAL) is a problem learning an action
localization model with only video-level labels available. In recent years, many WTAL …

Weakly-supervised temporal action localization aims to localize action instances from
untrimmed videos with only video-level labels. Due to the lack of frame-wise annotations …

Action learning is a research area that aims to recognize the action category of each frame
in the video. Context information is crucial for learning actions, but most existing methods …

Weakly supervised temporal action localization (WTAL) aims to localize action instances
with only video-level labels for supervision. Recent methods convert category labels to …

Weakly-supervised temporal action localization aims to train an accurate and robust
localization model using only video-level labels. Due to the lack of frame-level temporal …