Search Results for author:
Found 9 papers, 5 papers with code
Unsupervised Monocular Depth Learning in Dynamic Scenes
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.
What Matters in Unsupervised Optical Flow
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.
Ranked #5 on
Optical Flow Estimation
on Sintel Clean unsupervised
Taskology: Utilizing Task Relations at Scale
Many computer vision tasks address the problem of scene understanding and are naturally interrelated e. g. object classification, detection, scene segmentation, depth estimation, etc.
Improving Semantic Segmentation through Spatio-Temporal Consistency Learned from Videos
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.
Detecting Deficient Coverage in Colonoscopies
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.
Computationally Efficient Neural Image Compression
Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG).
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
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.
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
We present MorphNet, an approach to automate the design of neural network structures.
Scalable Learning of Non-Decomposable Objectives
Modern retrieval systems are often driven by an underlying machine learning model.
