Temporal action segmentation: An analysis of modern techniques
Temporal action segmentation (TAS) in videos aims at densely identifying video frames in
minutes-long videos with multiple action classes. As a long-range video understanding task …
minutes-long videos with multiple action classes. As a long-range video understanding task …
Deep learning-based action detection in untrimmed videos: A survey
Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …
applications, and is critical for video analysis. Despite the progress of action recognition …
Howto100m: Learning a text-video embedding by watching hundred million narrated video clips
Learning text-video embeddings usually requires a dataset of video clips with manually
provided captions. However, such datasets are expensive and time consuming to create and …
provided captions. However, such datasets are expensive and time consuming to create and …
Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-
KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M …
KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M …
Ms-tcn: Multi-stage temporal convolutional network for action segmentation
Temporally locating and classifying action segments in long untrimmed videos is of
particular interest to many applications like surveillance and robotics. While traditional …
particular interest to many applications like surveillance and robotics. While traditional …
Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation
This paper introduces a unified framework for video action segmentation via sequence to
sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to …
sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to …
Human action recognition and prediction: A survey
Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …
have been moving from inferring the present state to predicting the future state. Vision-based …
Neural sign language translation
Abstract Sign Language Recognition (SLR) has been an active research field for the last two
decades. However, most research to date has considered SLR as a naive gesture …
decades. However, most research to date has considered SLR as a naive gesture …
Temporal convolutional networks for action segmentation and detection
The ability to identify and temporally segment fine-grained human actions throughout a
video is crucial for robotics, surveillance, education, and beyond. Typical approaches …
video is crucial for robotics, surveillance, education, and beyond. Typical approaches …
Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization
K Kumar Singh, Y Jae Lee - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
Abstract We propose'Hide-and-Seek', a weakly-supervised framework that aims to improve
object localization in images and action localization in videos. Most existing weakly …
object localization in images and action localization in videos. Most existing weakly …