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Found 8 papers, 1 papers with code
A Step Toward More Inclusive People Annotations for Fairness
In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images.
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1. 58M action labels with multiple labels per person occurring frequently.
Ranked #2 on
Temporal Action Localization
on UCF101-24
Motion Prediction Under Multimodality with Conditional Stochastic Networks
In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables.
SfM-Net: Learning of Structure and Motion from Video
We propose SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations.
Discovering the Physical Parts of an Articulated Object Class From Multiple Videos
We propose a motion-based method to discover the physical parts of an articulated object class (e. g. head/torso/leg of a horse) from multiple videos.
Behavior Discovery and Alignment of Articulated Object Classes from Unstructured Video
On behavior discovery, we outperform the state-of-the-art Improved DTF descriptor.
Recovering Spatiotemporal Correspondence between Deformable Objects by Exploiting Consistent Foreground Motion in Video
Given unstructured videos of deformable objects, we automatically recover spatiotemporal correspondences to map one object to another (such as animals in the wild).
Articulated motion discovery using pairs of trajectories
We propose an unsupervised approach for discovering characteristic motion patterns in videos of highly articulated objects performing natural, unscripted behaviors, such as tigers in the wild.
