3,329 research outputs found
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
Adversarial nets with perceptual losses for text-to-image synthesis
Recent approaches in generative adversarial networks (GANs) can automatically
synthesize realistic images from descriptive text. Despite the overall fair
quality, the generated images often expose visible flaws that lack structural
definition for an object of interest. In this paper, we aim to extend state of
the art for GAN-based text-to-image synthesis by improving perceptual quality
of generated images. Differentiated from previous work, our synthetic image
generator optimizes on perceptual loss functions that measure pixel, feature
activation, and texture differences against a natural image. We present
visually more compelling synthetic images of birds and flowers generated from
text descriptions in comparison to some of the most prominent existing work
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Existing deep convolutional neural networks (CNNs) require a fixed-size
(e.g., 224x224) input image. This requirement is "artificial" and may reduce
the recognition accuracy for the images or sub-images of an arbitrary
size/scale. In this work, we equip the networks with another pooling strategy,
"spatial pyramid pooling", to eliminate the above requirement. The new network
structure, called SPP-net, can generate a fixed-length representation
regardless of image size/scale. Pyramid pooling is also robust to object
deformations. With these advantages, SPP-net should in general improve all
CNN-based image classification methods. On the ImageNet 2012 dataset, we
demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures
despite their different designs. On the Pascal VOC 2007 and Caltech101
datasets, SPP-net achieves state-of-the-art classification results using a
single full-image representation and no fine-tuning.
The power of SPP-net is also significant in object detection. Using SPP-net,
we compute the feature maps from the entire image only once, and then pool
features in arbitrary regions (sub-images) to generate fixed-length
representations for training the detectors. This method avoids repeatedly
computing the convolutional features. In processing test images, our method is
24-102x faster than the R-CNN method, while achieving better or comparable
accuracy on Pascal VOC 2007.
In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our
methods rank #2 in object detection and #3 in image classification among all 38
teams. This manuscript also introduces the improvement made for this
competition.Comment: This manuscript is the accepted version for IEEE Transactions on
Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo
ImageNet Large Scale Visual Recognition Challenge
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in
object category classification and detection on hundreds of object categories
and millions of images. The challenge has been run annually from 2010 to
present, attracting participation from more than fifty institutions.
This paper describes the creation of this benchmark dataset and the advances
in object recognition that have been possible as a result. We discuss the
challenges of collecting large-scale ground truth annotation, highlight key
breakthroughs in categorical object recognition, provide a detailed analysis of
the current state of the field of large-scale image classification and object
detection, and compare the state-of-the-art computer vision accuracy with human
accuracy. We conclude with lessons learned in the five years of the challenge,
and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL
VOC (per-category comparisons in Table 3, distribution of localization
difficulty in Fig 16), a list of queries used for obtaining object detection
images (Appendix C), and some additional reference
The FOLE Table
This paper continues the discussion of the representation of ontologies in
the first-order logical environment FOLE. According to Gruber, an ontology
defines the primitives with which to model the knowledge resources for a
community of discourse. These primitives, consisting of classes, relationships
and properties, are represented by the entity-relationship-attribute ERA data
model of Chen. An ontology uses formal axioms to constrain the interpretation
of these primitives. In short, an ontology specifies a logical theory. A series
of three papers by the author provide a rigorous mathematical representation
for the ERA data model in particular, and ontologies in general, within FOLE.
The first two papers, which provide a foundation and superstructure for FOLE,
represent the formalism and semantics of (many-sorted) first-order logic in a
classification form corresponding to ideas discussed in the Information Flow
Framework (IFF). The third paper will define an interpretation of FOLE in terms
of the transformational passage, first described in (Kent, 2013), from the
classification form of first-order logic to an equivalent interpretation form,
thereby defining the formalism and semantics of first-order logical/relational
database systems. Two papers will provide a precise mathematical basis for FOLE
interpretation: the current paper develops the notion of a FOLE relational
table following the relational model of Codd, and a follow-up paper will
develop the notion of a FOLE relational database. Both of these papers expand
on material found in the paper (Kent, 2011). Although the classification form
follows the entity-relationship-attribute data model of Chen, the
interpretation form follows the relational data model of Codd. In general, the
FOLE representation uses a conceptual structures approach, that is completely
compatible with formal concept analysis and information flow.Comment: 48 pages, 21 figures, 9 tables, submitted to T.A.C. for review in
August 201
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