Vision transformers are emerging as a powerful tool to solve computer vision problems.
Recent techniques have also proven the efficacy of transformers beyond the image domain …

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 …

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

Traditional video action detectors typically adopt the two-stage pipeline, where a person
detector is first employed to yield actor boxes and then 3D RoIAlign is used to extract actor …

Recent proposed neural network-based Temporal Action Detection (TAD) models are
inherently limited to extracting the discriminative representations and modeling action …

Action detection aims to localize the starting and ending points of action instances in
untrimmed videos and predict the classes of those instances. In this paper we make the …

Boundary localization is a challenging problem in Temporal Action Detection (TAD), in
which there are two main issues. First, the submergence of movement feature, ie the …

Abstract Vision Transformer (ViT) has shown high potential in video recognition owing to its
flexible design adaptable self-attention mechanisms and the efficacy of masked pre-training …

Robot-assisted prostate biopsy is a new technology to diagnose prostate cancer, but its
safety is influenced by the inability of robots to sense the tool-tissue interaction force …

Temporal action detection is a very important task in video understanding, aiming at
predicting the start and end time boundaries of all action instances in an unedited video and …