409 research outputs found
Video Acceleration Magnification
The ability to amplify or reduce subtle image changes over time is useful in
contexts such as video editing, medical video analysis, product quality control
and sports. In these contexts there is often large motion present which
severely distorts current video amplification methods that magnify change
linearly. In this work we propose a method to cope with large motions while
still magnifying small changes. We make the following two observations: i)
large motions are linear on the temporal scale of the small changes; ii) small
changes deviate from this linearity. We ignore linear motion and propose to
magnify acceleration. Our method is pure Eulerian and does not require any
optical flow, temporal alignment or region annotations. We link temporal
second-order derivative filtering to spatial acceleration magnification. We
apply our method to moving objects where we show motion magnification and color
magnification. We provide quantitative as well as qualitative evidence for our
method while comparing to the state-of-the-art.Comment: Accepted paper at CVPR 2017. Project webpage:
http://acceleration-magnification.github.io
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
End-to-End Chess Recognition
Chess recognition refers to the task of identifying the chess pieces
configuration from a chessboard image. Contrary to the predominant approach
that aims to solve this task through the pipeline of chessboard detection,
square localization, and piece classification, we rely on the power of deep
learning models and introduce two novel methodologies to circumvent this
pipeline and directly predict the chessboard configuration from the entire
image. In doing so, we avoid the inherent error accumulation of the sequential
approaches and the need for intermediate annotations. Furthermore, we introduce
a new dataset, Chess Recognition Dataset (ChessReD), specifically designed for
chess recognition that consists of 10,800 images and their corresponding
annotations. In contrast to existing synthetic datasets with limited angles,
this dataset comprises a diverse collection of real images of chess formations
captured from various angles using smartphone cameras; a sensor choice made to
ensure real-world applicability. We use this dataset to both train our model
and evaluate and compare its performance to that of the current
state-of-the-art. Our approach in chess recognition on this new benchmark
dataset outperforms related approaches, achieving a board recognition accuracy
of 15.26% (7x better than the current state-of-the-art).Comment: 9 page
What Affects Learned Equivariance in Deep Image Recognition Models?
Equivariance w.r.t. geometric transformations in neural networks improves
data efficiency, parameter efficiency and robustness to out-of-domain
perspective shifts. When equivariance is not designed into a neural network,
the network can still learn equivariant functions from the data. We quantify
this learned equivariance, by proposing an improved measure for equivariance.
We find evidence for a correlation between learned translation equivariance and
validation accuracy on ImageNet. We therefore investigate what can increase the
learned equivariance in neural networks, and find that data augmentation,
reduced model capacity and inductive bias in the form of convolutions induce
higher learned equivariance in neural networks.Comment: Accepted at CVPR workshop L3D-IVU 202
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