4,142 research outputs found
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis
What is not where: the challenge of integrating spatial representations into deep learning architectures
This paper examines to what degree current deep learning architectures for
image caption generation capture spatial language. On the basis of the
evaluation of examples of generated captions from the literature we argue that
systems capture what objects are in the image data but not where these objects
are located: the captions generated by these systems are the output of a
language model conditioned on the output of an object detector that cannot
capture fine-grained location information. Although language models provide
useful knowledge for image captions, we argue that deep learning image
captioning architectures should also model geometric relations between objects.Comment: 15 pages, 10 figures, Appears in CLASP Papers in Computational
Linguistics Vol 1: Proceedings of the Conference on Logic and Machine
Learning in Natural Language (LaML 2017), pp. 41-5
What Is Not Where: the Challenge of Integrating Spatial Representations Into Deep Learning Architectures
This paper examines to what degree current deep learning architectures for image caption generation capture spatial lan- guage. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the cap- tions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric rela- tions between objects
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