59,559 research outputs found
A Diagram Is Worth A Dozen Images
Diagrams are common tools for representing complex concepts, relationships
and events, often when it would be difficult to portray the same information
with natural images. Understanding natural images has been extensively studied
in computer vision, while diagram understanding has received little attention.
In this paper, we study the problem of diagram interpretation and reasoning,
the challenging task of identifying the structure of a diagram and the
semantics of its constituents and their relationships. We introduce Diagram
Parse Graphs (DPG) as our representation to model the structure of diagrams. We
define syntactic parsing of diagrams as learning to infer DPGs for diagrams and
study semantic interpretation and reasoning of diagrams in the context of
diagram question answering. We devise an LSTM-based method for syntactic
parsing of diagrams and introduce a DPG-based attention model for diagram
question answering. We compile a new dataset of diagrams with exhaustive
annotations of constituents and relationships for over 5,000 diagrams and
15,000 questions and answers. Our results show the significance of our models
for syntactic parsing and question answering in diagrams using DPGs
Solving Visual Madlibs with Multiple Cues
This paper focuses on answering fill-in-the-blank style multiple choice
questions from the Visual Madlibs dataset. Previous approaches to Visual
Question Answering (VQA) have mainly used generic image features from networks
trained on the ImageNet dataset, despite the wide scope of questions. In
contrast, our approach employs features derived from networks trained for
specialized tasks of scene classification, person activity prediction, and
person and object attribute prediction. We also present a method for selecting
sub-regions of an image that are relevant for evaluating the appropriateness of
a putative answer. Visual features are computed both from the whole image and
from local regions, while sentences are mapped to a common space using a simple
normalized canonical correlation analysis (CCA) model. Our results show a
significant improvement over the previous state of the art, and indicate that
answering different question types benefits from examining a variety of image
cues and carefully choosing informative image sub-regions
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Rich and dense human labeled datasets are among the main enabling factors for
the recent advance on vision-language understanding. Many seemingly distant
annotations (e.g., semantic segmentation and visual question answering (VQA))
are inherently connected in that they reveal different levels and perspectives
of human understandings about the same visual scenes --- and even the same set
of images (e.g., of COCO). The popularity of COCO correlates those annotations
and tasks. Explicitly linking them up may significantly benefit both individual
tasks and the unified vision and language modeling. We present the preliminary
work of linking the instance segmentations provided by COCO to the questions
and answers (QAs) in the VQA dataset, and name the collected links visual
questions and segmentation answers (VQS). They transfer human supervision
between the previously separate tasks, offer more effective leverage to
existing problems, and also open the door for new research problems and models.
We study two applications of the VQS data in this paper: supervised attention
for VQA and a novel question-focused semantic segmentation task. For the
former, we obtain state-of-the-art results on the VQA real multiple-choice task
by simply augmenting the multilayer perceptrons with some attention features
that are learned using the segmentation-QA links as explicit supervision. To
put the latter in perspective, we study two plausible methods and compare them
to an oracle method assuming that the instance segmentations are given at the
test stage.Comment: To appear on ICCV 201
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