In this paper, we present a one-stage framework TriDet for temporal action detection.
Existing methods often suffer from imprecise boundary predictions due to the ambiguous …

Self-attention based Transformer models have demonstrated impressive results for image
classification and object detection, and more recently for video understanding. Inspired by …

While large-scale image-text pretrained models such as CLIP have been used for multiple
video-level tasks on trimmed videos, their use for temporal localization in untrimmed videos …

With the explosive growth of videos, weakly-supervised temporal action localization (WS-
TAL) task has become a promising research direction in pattern analysis and machine …

Weakly-supervised temporal action localization aims to localize and recognize actions in
untrimmed videos with only video-level category labels during training. Without instance …

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 …

This work aims at advancing temporal action detection (TAD) using an encoder-decoder
framework with action queries, similar to DETR, which has shown great success in object …

Abstract Temporal Action Detection (TAD) aims to identify the action boundaries and the
corresponding category within untrimmed videos. Inspired by the success of DETR in object …

We propose a new formulation of temporal action detection (TAD) with denoising diffusion,
DiffTAD in short. Taking as input random temporal proposals, it can yield action proposals …