A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective
Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …
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 …
Videomae v2: Scaling video masked autoencoders with dual masking
Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …
generalize to a variety of downstream tasks. However, it is still challenging to train video …
Tridet: Temporal action detection with relative boundary modeling
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 …
Existing methods often suffer from imprecise boundary predictions due to the ambiguous …
Ego4d: Around the world in 3,000 hours of egocentric video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …
Actionformer: Localizing moments of actions with transformers
Self-attention based Transformer models have demonstrated impressive results for image
classification and object detection, and more recently for video understanding. Inspired by …
classification and object detection, and more recently for video understanding. Inspired by …
Prompting visual-language models for efficient video understanding
Image-based visual-language (I-VL) pre-training has shown great success for learning joint
visual-textual representations from large-scale web data, revealing remarkable ability for …
visual-textual representations from large-scale web data, revealing remarkable ability for …
Learning salient boundary feature for anchor-free temporal action localization
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 …
Typically, such a task aims at inferring both the action category and localization of the start …
End-to-end temporal action detection with transformer
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 …
interval of every action instance in an untrimmed video. It is a fundamental and challenging …
Self-supervised learning by cross-modal audio-video clustering
Visual and audio modalities are highly correlated, yet they contain different information.
Their strong correlation makes it possible to predict the semantics of one from the other with …
Their strong correlation makes it possible to predict the semantics of one from the other with …