76,401 research outputs found
Adaptive Object Detection Using Adjacency and Zoom Prediction
State-of-the-art object detection systems rely on an accurate set of region
proposals. Several recent methods use a neural network architecture to
hypothesize promising object locations. While these approaches are
computationally efficient, they rely on fixed image regions as anchors for
predictions. In this paper we propose to use a search strategy that adaptively
directs computational resources to sub-regions likely to contain objects.
Compared to methods based on fixed anchor locations, our approach naturally
adapts to cases where object instances are sparse and small. Our approach is
comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach
while using two orders of magnitude fewer anchors on average. Code is publicly
available.Comment: Accepted to CVPR 201
Object Level Deep Feature Pooling for Compact Image Representation
Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.Comment: Deep Vision 201
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