3,350 research outputs found
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image
representations without manual supervision. We use motion cues in the form of
optical flow, to supervise representations of static images. The obvious
approach of training a network to predict flow from a single image can be
needlessly difficult due to intrinsic ambiguities in this prediction task. We
instead propose a much simpler learning goal: embed pixels such that the
similarity between their embeddings matches that between their optical flow
vectors. At test time, the learned deep network can be used without access to
video or flow information and transferred to tasks such as image
classification, detection, and segmentation. Our method, which significantly
simplifies previous attempts at using motion for self-supervision, achieves
state-of-the-art results in self-supervision using motion cues, competitive
results for self-supervision in general, and is overall state of the art in
self-supervised pretraining for semantic image segmentation, as demonstrated on
standard benchmarks
Joint Optical Flow and Temporally Consistent Semantic Segmentation
The importance and demands of visual scene understanding have been steadily
increasing along with the active development of autonomous systems.
Consequently, there has been a large amount of research dedicated to semantic
segmentation and dense motion estimation. In this paper, we propose a method
for jointly estimating optical flow and temporally consistent semantic
segmentation, which closely connects these two problem domains and leverages
each other. Semantic segmentation provides information on plausible physical
motion to its associated pixels, and accurate pixel-level temporal
correspondences enhance the accuracy of semantic segmentation in the temporal
domain. We demonstrate the benefits of our approach on the KITTI benchmark,
where we observe performance gains for flow and segmentation. We achieve
state-of-the-art optical flow results, and outperform all published algorithms
by a large margin on challenging, but crucial dynamic objects.Comment: 14 pages, Accepted for CVRSUAD workshop at ECCV 201
Automated segmentation on the entire cardiac cycle using a deep learning work-flow
The segmentation of the left ventricle (LV) from CINE MRI images is essential
to infer important clinical parameters. Typically, machine learning algorithms
for automated LV segmentation use annotated contours from only two cardiac
phases, diastole, and systole. In this work, we present an analysis work-flow
for fully-automated LV segmentation that learns from images acquired through
the cardiac cycle. The workflow consists of three components: first, for each
image in the sequence, we perform an automated localization and subsequent
cropping of the bounding box containing the cardiac silhouette. Second, we
identify the LV contours using a Temporal Fully Convolutional Neural Network
(T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through a
recurrent mechanism enforcing temporal coherence across consecutive frames.
Finally, we further defined the boundaries using either one of two components:
fully-connected Conditional Random Fields (CRFs) with Gaussian edge potentials
and Semantic Flow. Our initial experiments suggest that significant improvement
in performance can potentially be achieved by using a recurrent neural network
component that explicitly learns cardiac motion patterns whilst performing LV
segmentation.Comment: 6 pages, 2 figures, published on IEEE Xplor
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