3,407 research outputs found
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Question categorization and expert retrieval methods have been crucial for
information organization and accessibility in community question & answering
(CQA) platforms. Research in this area, however, has dealt with only the text
modality. With the increasing multimodal nature of web content, we focus on
extending these methods for CQA questions accompanied by images. Specifically,
we leverage the success of representation learning for text and images in the
visual question answering (VQA) domain, and adapt the underlying concept and
architecture for automated category classification and expert retrieval on
image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of
Yahoo! Answers.
To the best of our knowledge, this is the first work to tackle the
multimodality challenge in CQA, and to adapt VQA models for tasks on a more
ecologically valid source of visual questions. Our analysis of the differences
between visual QA and community QA data drives our proposal of novel
augmentations of an attention method tailored for CQA, and use of auxiliary
tasks for learning better grounding features. Our final model markedly
outperforms the text-only and VQA model baselines for both tasks of
classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
Learning to follow human instructions is a long-pursued goal in artificial
intelligence. The task becomes particularly challenging if no prior knowledge
of the employed language is assumed while relying only on a handful of examples
to learn from. Work in the past has relied on hand-coded components or manually
engineered features to provide strong inductive biases that make learning in
such situations possible. In contrast, here we seek to establish whether this
knowledge can be acquired automatically by a neural network system through a
two phase training procedure: A (slow) offline learning stage where the network
learns about the general structure of the task and a (fast) online adaptation
phase where the network learns the language of a new given speaker. Controlled
experiments show that when the network is exposed to familiar instructions but
containing novel words, the model adapts very efficiently to the new
vocabulary. Moreover, even for human speakers whose language usage can depart
significantly from our artificial training language, our network can still make
use of its automatically acquired inductive bias to learn to follow
instructions more effectively
Context Embedding Networks
Low dimensional embeddings that capture the main variations of interest in
collections of data are important for many applications. One way to construct
these embeddings is to acquire estimates of similarity from the crowd. However,
similarity is a multi-dimensional concept that varies from individual to
individual. Existing models for learning embeddings from the crowd typically
make simplifying assumptions such as all individuals estimate similarity using
the same criteria, the list of criteria is known in advance, or that the crowd
workers are not influenced by the data that they see. To overcome these
limitations we introduce Context Embedding Networks (CENs). In addition to
learning interpretable embeddings from images, CENs also model worker biases
for different attributes along with the visual context i.e. the visual
attributes highlighted by a set of images. Experiments on two noisy crowd
annotated datasets show that modeling both worker bias and visual context
results in more interpretable embeddings compared to existing approaches.Comment: CVPR 2018 spotligh
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Towards solving computer vision problems: datasets, labels, algorithms, and applications
The solution to a supervised computer vision problem consists of an application, algorithm, input data, and a set of human generated labels. Solving these kinds of tasks involves collecting large quantities of data, collecting appropriate labels, and developing machine vision algorithms tailored to the application. Progress on these problems has often benefited from large scale datasets with high fidelity labels. Successful algorithms display a synergy between application goals and the size and quality of the dataset. This thesis presents work highlighting the importance of each component of a supervised vision task.First, the problem of automatically classifying groups of people into social categories is introduced. This problem is called Urban Tribe Classification. To tackle this problem, each individual and the entire group of individuals are modeled. Since this was a newly introduced computer vision problem, a dataset for this task was created. On this dataset, the combined representation of group and individuals outperforms using only the person representations. This model showed promising results for automatic subculture classification.Second, the problem of creating perceptual embeddings based on human similarity judgements is tackled. This work focuses on triplet similarity comparisons of the form ``Is object more similar to or ?'', which have been useful for computer vision and machine learning applications. Unfortunately, triplet similarity comparisons, like many human labeling efforts, can be prohibitively expensive. This work proposes two techniques for dealing with this obstacle. First, an alternative display for collecting triplets is designed. This display shows a probe image and a grid of query images, allowing the user to collect multiple triplets simultaneously. The display is shown to reduce the cost and time of triplet collection. In addition, higher quality embeddings are created with the improved triplet collection UI. A 10,000-food item dataset of human taste similarity was created using this UI. Second, ``SNaCK,'' a low-dimensional perceptual embedding algorithm that combines human expertise with automatic machine kernels, is introduced. Both parts are complementary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. Finally, the precise localization of key frames of an action is explored. This work focuses on detecting the exact starting frame of a behavior, an important task for neuroscience research. To address this problem, a loss designed to penalize extra and missed action start detections over small misalignments. Recurrent neural networks (RNN) are trained to optimize this loss. The model is shown to reduce the number of false positives, an important criteria defined by the neuroscientist. The performance of the model is evaluated on a new dataset, the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was created for neuroscience research. On this dataset, the proposed model outperforms related approaches and baseline methods using an unstructured loss
Kernel functions based on triplet comparisons
Given only information in the form of similarity triplets "Object A is more
similar to object B than to object C" about a data set, we propose two ways of
defining a kernel function on the data set. While previous approaches construct
a low-dimensional Euclidean embedding of the data set that reflects the given
similarity triplets, we aim at defining kernel functions that correspond to
high-dimensional embeddings. These kernel functions can subsequently be used to
apply any kernel method to the data set
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