19,590 research outputs found
Play and Learn: Using Video Games to Train Computer Vision Models
Video games are a compelling source of annotated data as they can readily
provide fine-grained groundtruth for diverse tasks. However, it is not clear
whether the synthetically generated data has enough resemblance to the
real-world images to improve the performance of computer vision models in
practice. We present experiments assessing the effectiveness on real-world data
of systems trained on synthetic RGB images that are extracted from a video
game. We collected over 60000 synthetic samples from a modern video game with
similar conditions to the real-world CamVid and Cityscapes datasets. We provide
several experiments to demonstrate that the synthetically generated RGB images
can be used to improve the performance of deep neural networks on both image
segmentation and depth estimation. These results show that a convolutional
network trained on synthetic data achieves a similar test error to a network
that is trained on real-world data for dense image classification. Furthermore,
the synthetically generated RGB images can provide similar or better results
compared to the real-world datasets if a simple domain adaptation technique is
applied. Our results suggest that collaboration with game developers for an
accessible interface to gather data is potentially a fruitful direction for
future work in computer vision.Comment: To appear in the British Machine Vision Conference (BMVC), September
2016. -v2: fixed a typo in the reference
CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering
Intrinsic image decomposition is a challenging, long-standing computer vision
problem for which ground truth data is very difficult to acquire. We explore
the use of synthetic data for training CNN-based intrinsic image decomposition
models, then applying these learned models to real-world images. To that end,
we present \ICG, a new, large-scale dataset of physically-based rendered images
of scenes with full ground truth decompositions. The rendering process we use
is carefully designed to yield high-quality, realistic images, which we find to
be crucial for this problem domain. We also propose a new end-to-end training
method that learns better decompositions by leveraging \ICG, and optionally IIW
and SAW, two recent datasets of sparse annotations on real-world images.
Surprisingly, we find that a decomposition network trained solely on our
synthetic data outperforms the state-of-the-art on both IIW and SAW, and
performance improves even further when IIW and SAW data is added during
training. Our work demonstrates the suprising effectiveness of
carefully-rendered synthetic data for the intrinsic images task.Comment: Paper for 'CGIntrinsics: Better Intrinsic Image Decomposition through
Physically-Based Rendering' published in ECCV, 201
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
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