344,327 research outputs found
An Analysis of Scale Invariance in Object Detection - SNIP
An analysis of different techniques for recognizing and detecting objects
under extreme scale variation is presented. Scale specific and scale invariant
design of detectors are compared by training them with different configurations
of input data. By evaluating the performance of different network architectures
for classifying small objects on ImageNet, we show that CNNs are not robust to
changes in scale. Based on this analysis, we propose to train and test
detectors on the same scales of an image-pyramid. Since small and large objects
are difficult to recognize at smaller and larger scales respectively, we
present a novel training scheme called Scale Normalization for Image Pyramids
(SNIP) which selectively back-propagates the gradients of object instances of
different sizes as a function of the image scale. On the COCO dataset, our
single model performance is 45.7% and an ensemble of 3 networks obtains an mAP
of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train
with bounding box supervision. Our submission won the Best Student Entry in the
COCO 2017 challenge. Code will be made available at
\url{http://bit.ly/2yXVg4c}.Comment: CVPR 2018, camera ready versio
A Novel Weight-Shared Multi-Stage CNN for Scale Robustness
Convolutional neural networks (CNNs) have demonstrated remarkable results in
image classification for benchmark tasks and practical applications. The CNNs
with deeper architectures have achieved even higher performance recently thanks
to their robustness to the parallel shift of objects in images as well as their
numerous parameters and the resulting high expression ability. However, CNNs
have a limited robustness to other geometric transformations such as scaling
and rotation. This limits the performance improvement of the deep CNNs, but
there is no established solution. This study focuses on scale transformation
and proposes a network architecture called the weight-shared multi-stage
network (WSMS-Net), which consists of multiple stages of CNNs. The proposed
WSMS-Net is easily combined with existing deep CNNs such as ResNet and DenseNet
and enables them to acquire robustness to object scaling. Experimental results
on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing
deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for
image classification tasks with only a minor increase in the number of
parameters and computation time.Comment: accepted version, 13 page
ELASTIC: Improving CNNs with Dynamic Scaling Policies
Scale variation has been a challenge from traditional to modern approaches in
computer vision. Most solutions to scale issues have a similar theme: a set of
intuitive and manually designed policies that are generic and fixed (e.g. SIFT
or feature pyramid). We argue that the scaling policy should be learned from
data. In this paper, we introduce ELASTIC, a simple, efficient and yet very
effective approach to learn a dynamic scale policy from data. We formulate the
scaling policy as a non-linear function inside the network's structure that (a)
is learned from data, (b) is instance specific, (c) does not add extra
computation, and (d) can be applied on any network architecture. We applied
ELASTIC to several state-of-the-art network architectures and showed consistent
improvement without extra (sometimes even lower) computation on ImageNet
classification, MSCOCO multi-label classification, and PASCAL VOC semantic
segmentation. Our results show major improvement for images with scale
challenges. Our code is available here: https://github.com/allenai/elasticComment: CVPR 2019 oral, code available https://github.com/allenai/elasti
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