688 research outputs found
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
In recent years, automatic generation of image descriptions (captions), that
is, image captioning, has attracted a great deal of attention. In this paper,
we particularly consider generating Japanese captions for images. Since most
available caption datasets have been constructed for English language, there
are few datasets for Japanese. To tackle this problem, we construct a
large-scale Japanese image caption dataset based on images from MS-COCO, which
is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions
for 164,062 images. In the experiment, we show that a neural network trained
using STAIR Captions can generate more natural and better Japanese captions,
compared to those generated using English-Japanese machine translation after
generating English captions.Comment: Accepted as ACL2017 short paper. 5 page
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
Automatic Concept Discovery from Parallel Text and Visual Corpora
Humans connect language and vision to perceive the world. How to build a
similar connection for computers? One possible way is via visual concepts,
which are text terms that relate to visually discriminative entities. We
propose an automatic visual concept discovery algorithm using parallel text and
visual corpora; it filters text terms based on the visual discriminative power
of the associated images, and groups them into concepts using visual and
semantic similarities. We illustrate the applications of the discovered
concepts using bidirectional image and sentence retrieval task and image
tagging task, and show that the discovered concepts not only outperform several
large sets of manually selected concepts significantly, but also achieves the
state-of-the-art performance in the retrieval task.Comment: To appear in ICCV 201
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
visual relationship is considered as a phrase with three components. We
formulate the visual relationship detection as three inter-connected
recognition problems and propose a Visual Phrase guided Convolutional Neural
Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
Phrase-guided Message Passing Structure (PMPS) to establish the connection
among relationship components and help the model consider the three problems
jointly. Corresponding non-maximum suppression method and model training
strategy are also proposed. Experimental results show that our ViP-CNN
outperforms the state-of-art method both in speed and accuracy. We further
pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
found to perform better than the pretraining on the ImageNet for this task.Comment: 10 pages, 5 figures, accepted by CVPR 201
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