As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

Temporal action localization is an important yet challenging task in video understanding.
Typically, such a task aims at inferring both the action category and localization of the start …

Temporal action detection (TAD) aims to determine the semantic label and the temporal
interval of every action instance in an untrimmed video. It is a fundamental and challenging …

We target at the task of weakly-supervised action localization (WSAL), where only video-
level action labels are available during model training. Despite the recent progress, existing …

Weakly-supervised temporal action localization (WS-TAL) aims to localize the action
instances and recognize their categories with only video-level labels. Despite great …

Most of the current action localization methods follow an anchor-based pipeline: depicting
action instances by pre-defined anchors, learning to select the anchors closest to the ground …

Due to the large memory footprint of untrimmed videos, current state-of-the-art video
localization methods operate atop precomputed video clip features. These features are …

The task of video moment retrieval (VMR) is to retrieve the specific video moment from an
untrimmed video, according to a textual query. It is a challenging task that requires effective …

In recent years, deep learning moves video-based Continuous Sign Language Recognition
(CSLR) significantly forward. Currently, a typical network combination for CSLR includes a …