Action Recognition In Still Images
1 papers with code • 0 benchmarks • 0 datasets
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Human Action Recognition in Still Images Using ConViT
Understanding the relationship between different parts of an image is crucial in a variety of applications, including object recognition, scene understanding, and image classification.
Ensembles of Deep Neural Networks for Action Recognition in Still Images
A big challenge in action recognition in still images is the lack of large enough datasets, which is problematic for training deep Convolutional Neural Networks (CNNs) due to the overfitting issue.
Zero-Shot Action Recognition in Videos: A Survey
Zero-Shot Action Recognition has attracted attention in the last years and many approaches have been proposed for recognition of objects, events and actions in images and videos.
Loss Guided Activation for Action Recognition in Still Images
This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features.
Temporal Hallucinating for Action Recognition With Few Still Images
To mimic this capacity, we propose a novel Hybrid Video Memory (HVM) machine, which can hallucinate temporal features of still images from video memory, in order to boost action recognition with few still images.
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition
Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding.
Face-space Action Recognition by Face-Object Interactions
Action recognition in still images has seen major improvement in recent years due to advances in human pose estimation, object recognition and stronger feature representations.
Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images
Recognizing actions from still images is popularly studied recently.
Hand-Object Interaction and Precise Localization in Transitive Action Recognition
In this paper we demonstrate how recognition is improved by obtaining precise localization of the action-object and consequently extracting details of the object shape together with the actor-object interaction.
Action recognition in still images by latent superpixel classification
In the proposed approach, the action class is predicted by a structural model(learnt by Latent Structural SVM) based on measurements from the image superpixels and their latent classes.