184 citations · 530 across the 17 of their papers we have counts for
18 papers
ORBIT++: Benchmarking SfM in the Wild with 360° Video
Sara Sabour, Linyi Jin, Richard Tucker +9
Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging camera motions or dynamic scenes.…
Object Learning and Robust 3D Reconstruction
Sara Sabour
In this thesis we discuss architectural designs and training methods for a neural network to have the ability of dissecting an image into objects of interest without supervision. T…
RoMo: Robust Motion Segmentation Improves Structure from Motion
Lily Goli, Sara Sabour, Mark Matthews +6
There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, th…
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
Sara Sabour, Lily Goli, George Kopanas +6
3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However…
RobustNeRF: Ignoring Distractors with Robust Losses
Sara Sabour, Suhani Vora, Daniel Duckworth +3
Neural radiance fields (NeRF) excel at synthesizing new views given multi-view, calibrated images of a static scene. When scenes include distractors, which are not persistent durin…
Testing GLOM's ability to infer wholes from ambiguous parts
Laura Culp, Sara Sabour, Geoffrey E. Hinton
The GLOM architecture proposed by Hinton [2021] is a recurrent neural network for parsing an image into a hierarchy of wholes and parts. When a part is ambiguous, GLOM assumes that…
nerf2nerf: Pairwise Registration of Neural Radiance Fields
Lily Goli, Daniel Rebain, Sara Sabour +2
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (Ne…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
Conditional Object-Centric Learning from Video
Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran +6
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Rec…
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun, Andrea Tagliasacchi, Boyang Deng +4
We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise th…
Unsupervised part representation by Flow Capsules
Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani +2
Capsule networks aim to parse images into a hierarchy of objects, parts and relations. While promising, they remain limited by an inability to learn effective low level part descri…
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Yao Qin, Nicholas Frosst, Sara Sabour +3
Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. In this paper, we first detect adversarial examples or…
Stacked Capsule Autoencoders
Adam R. Kosiorek, Sara Sabour, Yee Whye Teh +1
Objects are composed of a set of geometrically organized parts. We introduce an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships between parts…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules
Nicholas Frosst, Sara Sabour, Geoffrey Hinton
We present a simple technique that allows capsule models to detect adversarial images. In addition to being trained to classify images, the capsule model is trained to reconstruct…
Optimal Completion Distillation for Sequence Learning
Sara Sabour, William Chan, Mohammad Norouzi
We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters…
Dynamic Routing Between Capsules
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton
A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of…
Adversarial Manipulation of Deep Representations
Sara Sabour, Yanshuai Cao, Fartash Faghri +1
We show that the representation of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations t…