Search Results for author:
Found 7 papers, 2 papers with code
Sapiens: Foundation for Human Vision Models
We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction.
Ranked #1 on
Keypoint Detection
on MS COCO
(Validation AP metric, using extra
training data)
Theseus: A Library for Differentiable Nonlinear Optimization
We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision.
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Robustness evaluations like our checklist will be crucial in future safety evaluations of visual perception modules, and be useful for a wide range of stakeholders including designers, deployers, and regulators involved in the certification of these systems.
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.
First demonstration of early warning gravitational wave alerts
We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger.
High Energy Astrophysical Phenomena
Learning Tactile Models for Factor Graph-based Estimation
In order to incorporate tactile measurements in the graph, we need local observation models that can map high-dimensional tactile images onto a low-dimensional state space.
Towards high-throughput 3D insect capture for species discovery and diagnostics
Digitisation of natural history collections not only preserves precious information about biological diversity, it also enables us to share, analyse, annotate and compare specimens to gain new insights.
