3,900 research outputs found
Semantic Video CNNs through Representation Warping
In this work, we propose a technique to convert CNN models for semantic
segmentation of static images into CNNs for video data. We describe a warping
method that can be used to augment existing architectures with very little
extra computational cost. This module is called NetWarp and we demonstrate its
use for a range of network architectures. The main design principle is to use
optical flow of adjacent frames for warping internal network representations
across time. A key insight of this work is that fast optical flow methods can
be combined with many different CNN architectures for improved performance and
end-to-end training. Experiments validate that the proposed approach incurs
only little extra computational cost, while improving performance, when video
streams are available. We achieve new state-of-the-art results on the CamVid
and Cityscapes benchmark datasets and show consistent improvements over
different baseline networks. Our code and models will be available at
http://segmentation.is.tue.mpg.deComment: ICCV 201
Deep Extreme Cut: From Extreme Points to Object Segmentation
This paper explores the use of extreme points in an object (left-most,
right-most, top, bottom pixels) as input to obtain precise object segmentation
for images and videos. We do so by adding an extra channel to the image in the
input of a convolutional neural network (CNN), which contains a Gaussian
centered in each of the extreme points. The CNN learns to transform this
information into a segmentation of an object that matches those extreme points.
We demonstrate the usefulness of this approach for guided segmentation
(grabcut-style), interactive segmentation, video object segmentation, and dense
segmentation annotation. We show that we obtain the most precise results to
date, also with less user input, in an extensive and varied selection of
benchmarks and datasets. All our models and code are publicly available on
http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/.Comment: CVPR 2018 camera ready. Project webpage and code:
http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr
Lucid Data Dreaming for Video Object Segmentation
Convolutional networks reach top quality in pixel-level video object
segmentation but require a large amount of training data (1k~100k) to deliver
such results. We propose a new training strategy which achieves
state-of-the-art results across three evaluation datasets while using 20x~1000x
less annotated data than competing methods. Our approach is suitable for both
single and multiple object segmentation. Instead of using large training sets
hoping to generalize across domains, we generate in-domain training data using
the provided annotation on the first frame of each video to synthesize ("lucid
dream") plausible future video frames. In-domain per-video training data allows
us to train high quality appearance- and motion-based models, as well as tune
the post-processing stage. This approach allows to reach competitive results
even when training from only a single annotated frame, without ImageNet
pre-training. Our results indicate that using a larger training set is not
automatically better, and that for the video object segmentation task a smaller
training set that is closer to the target domain is more effective. This
changes the mindset regarding how many training samples and general
"objectness" knowledge are required for the video object segmentation task.Comment: Accepted in International Journal of Computer Vision (IJCV
Piano Crossing - Walking on a Keyboard
Piano Crossing is an interactive art installation which turns a pedestrian crossing marked with white stripes into a piano keyboard so that pedestrians can generate music by walking over it. Matching tones are generated when a pedestrian is over a particular stripe or key. A digital camera is directed at the crossing from above. A special computer vision application was developed that maps the stripes of the pedestrian crossing to piano keys and which detects over which key is the center of gravity of every pedestrian in the image at any given moment. Special black stripes are added to the crossing, which represent also the black piano keys. The application consists of two parts: (1) initialization, where the model of the abstract piano keyboard is mapped to the image of the pedestrian crossing and (2) the detection of pedestrians on the crossing so that musical tones can be generated according to their locations. The art installation Piano crossing was presented to the public for the first time during the 51st Jazz Festival in Ljubljana in July 2010
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