6 research outputs found
S3Aug: Segmentation, Sampling, and Shift for Action Recognition
Action recognition is a well-established area of research in computer vision.
In this paper, we propose S3Aug, a video data augmenatation for action
recognition. Unlike conventional video data augmentation methods that involve
cutting and pasting regions from two videos, the proposed method generates new
videos from a single training video through segmentation and label-to-image
transformation. Furthermore, the proposed method modifies certain categories of
label images by sampling to generate a variety of videos, and shifts
intermediate features to enhance the temporal coherency between frames of the
generate videos. Experimental results on the UCF101, HMDB51, and Mimetics
datasets demonstrate the effectiveness of the proposed method, paricularlly for
out-of-context videos of the Mimetics dataset
Just Add ! Pose Induced Video Transformers for Understanding Activities of Daily Living
Video transformers have become the de facto standard for human action
recognition, yet their exclusive reliance on the RGB modality still limits
their adoption in certain domains. One such domain is Activities of Daily
Living (ADL), where RGB alone is not sufficient to distinguish between visually
similar actions, or actions observed from multiple viewpoints. To facilitate
the adoption of video transformers for ADL, we hypothesize that the
augmentation of RGB with human pose information, known for its sensitivity to
fine-grained motion and multiple viewpoints, is essential. Consequently, we
introduce the first Pose Induced Video Transformer: PI-ViT (or -ViT), a
novel approach that augments the RGB representations learned by video
transformers with 2D and 3D pose information. The key elements of -ViT are
two plug-in modules, 2D Skeleton Induction Module and 3D Skeleton Induction
Module, that are responsible for inducing 2D and 3D pose information into the
RGB representations. These modules operate by performing pose-aware auxiliary
tasks, a design choice that allows -ViT to discard the modules during
inference. Notably, -ViT achieves the state-of-the-art performance on
three prominent ADL datasets, encompassing both real-world and large-scale
RGB-D datasets, without requiring poses or additional computational overhead at
inference.Comment: Code and models will be released at:
https://github.com/dominickrei/pi-vi