Search Results for author: Ariel Gordon

Found 9 papers, 5 papers with code

Unsupervised Monocular Depth Learning in Dynamic Scenes

5 code implementations30 Oct 2020 Hanhan Li, Ariel Gordon, Hang Zhao, Vincent Casser, Anelia Angelova

We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consistency being the sole source of supervision.

Depth Prediction Monocular Depth Estimation +2

What Matters in Unsupervised Optical Flow

5 code implementations ECCV 2020 Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, Anelia Angelova

We systematically compare and analyze a set of key components in unsupervised optical flow to identify which photometric loss, occlusion handling, and smoothness regularization is most effective.

Occlusion Handling Optical Flow Estimation

Taskology: Utilizing Task Relations at Scale

no code implementations CVPR 2021 Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon

Many computer vision tasks address the problem of scene understanding and are naturally interrelated e. g. object classification, detection, scene segmentation, depth estimation, etc.

Depth Estimation Motion Estimation +4

Improving Semantic Segmentation through Spatio-Temporal Consistency Learned from Videos

no code implementations11 Apr 2020 Ankita Pasad, Ariel Gordon, Tsung-Yi Lin, Anelia Angelova

We leverage unsupervised learning of depth, egomotion, and camera intrinsics to improve the performance of single-image semantic segmentation, by enforcing 3D-geometric and temporal consistency of segmentation masks across video frames.

Segmentation Semantic Segmentation

Detecting Deficient Coverage in Colonoscopies

no code implementations23 Jan 2020 Daniel Freedman, Yochai Blau, Liran Katzir, Amit Aides, Ilan Shimshoni, Danny Veikherman, Tomer Golany, Ariel Gordon, Greg Corrado, Yossi Matias, Ehud Rivlin

Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.

Depth Estimation

Computationally Efficient Neural Image Compression

no code implementations18 Dec 2019 Nick Johnston, Elad Eban, Ariel Gordon, Johannes Ballé

Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG).

Decoder Image Compression

Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras

4 code implementations ICCV 2019 Ariel Gordon, Hanhan Li, Rico Jonschkowski, Anelia Angelova

We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal.

Depth Prediction Monocular Depth Estimation +1

Scalable Learning of Non-Decomposable Objectives

2 code implementations16 Aug 2016 Elad ET. Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Rif A. Saurous, Gal Elidan

Modern retrieval systems are often driven by an underlying machine learning model.


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