no code implementations • 25 Jan 2024 • Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen
First, a text-guided approach is used to extract a person-diversity representation from a pre-trained image-text model.
no code implementations • NeurIPS 2023 • Candice Schumann, Gbolahan O. Olanubi, Auriel Wright, Ellis Monk Jr., Courtney Heldreth, Susanna Ricco
Our study shows that annotators can reliably annotate skin tone in a way that aligns with an expert in the MST scale, even under challenging environmental conditions.
no code implementations • 5 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.
8 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.
Ranked #6 on Action Detection on UCF101-24
no code implementations • 5 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.
no code implementations • 25 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.
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.
no code implementations • 30 Nov 2015 • Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari
On behavior discovery, we outperform the state-of-the-art Improved DTF descriptor.
no code implementations • 1 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).
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.