Search Results for author: Susanna Ricco

Found 8 papers, 1 papers with code

A Step Toward More Inclusive People Annotations for Fairness

no code implementations5 May 2021 Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, Caroline Pantofaru

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.

Fairness

AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions

4 code implementations CVPR 2018 Chunhui Gu, Chen Sun, David A. Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, Jitendra Malik

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.

Action Recognition Video Understanding

Motion Prediction Under Multimodality with Conditional Stochastic Networks

no code implementations5 May 2017 Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, Rahul Sukthankar

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.

motion prediction Optical Flow Estimation +2

SfM-Net: Learning of Structure and Motion from Video

no code implementations25 Apr 2017 Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar, Katerina Fragkiadaki

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.

Motion Estimation Optical Flow Estimation

Discovering the Physical Parts of an Articulated Object Class From Multiple Videos

no code implementations CVPR 2016 Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

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.

Motion Segmentation

Recovering Spatiotemporal Correspondence between Deformable Objects by Exploiting Consistent Foreground Motion in Video

no code implementations1 Dec 2014 Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

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

no code implementations CVPR 2015 Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

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.

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