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1,400 | Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences
Using Deep Neural Networks | cs.LG | Deep neural network (DNN) models have recently obtained state-of-the-art
prediction accuracy for the transcription factor binding (TFBS) site
classification task. However, it remains unclear how these approaches identify
meaningful DNA sequence signals and give insights as to why TFs bind to certain
locations. In this ... | computer science |
1,401 | Applying Deep Learning to Basketball Trajectories | cs.NE | One of the emerging trends for sports analytics is the growing use of player
and ball tracking data. A parallel development is deep learning predictive
approaches that use vast quantities of data with less reliance on feature
engineering. This paper applies recurrent neural networks in the form of
sequence modeling to ... | computer science |
1,402 | Deep Convolutional Neural Networks and Data Augmentation for
Environmental Sound Classification | cs.SD | The ability of deep convolutional neural networks (CNN) to learn
discriminative spectro-temporal patterns makes them well suited to
environmental sound classification. However, the relative scarcity of labeled
data has impeded the exploitation of this family of high-capacity models. This
study has two primary contribut... | computer science |
1,403 | Crafting a multi-task CNN for viewpoint estimation | cs.CV | Convolutional Neural Networks (CNNs) were recently shown to provide
state-of-the-art results for object category viewpoint estimation. However
different ways of formulating this problem have been proposed and the competing
approaches have been explored with very different design choices. This paper
presents a compariso... | computer science |
1,404 | A Baseline for Detecting Misclassified and Out-of-Distribution Examples
in Neural Networks | cs.NE | We consider the two related problems of detecting if an example is
misclassified or out-of-distribution. We present a simple baseline that
utilizes probabilities from softmax distributions. Correctly classified
examples tend to have greater maximum softmax probabilities than erroneously
classified and out-of-distributi... | computer science |
1,405 | RetiNet: Automatic AMD identification in OCT volumetric data | cs.CV | Optical Coherence Tomography (OCT) provides a unique ability to image the eye
retina in 3D at micrometer resolution and gives ophthalmologist the ability to
visualize retinal diseases such as Age-Related Macular Degeneration (AMD).
While visual inspection of OCT volumes remains the main method for AMD
identification, d... | computer science |
1,406 | Mixed Neural Network Approach for Temporal Sleep Stage Classification | cs.CV | This paper proposes a practical approach to addressing limitations posed by
use of single active electrodes in applications for sleep stage classification.
Electroencephalography (EEG)-based characterizations of sleep stage progression
contribute the diagnosis and monitoring of the many pathologies of sleep.
Several pr... | computer science |
1,407 | Deep Neural Networks for HDR imaging | cs.CV | We propose novel methods of solving two tasks using Convolutional Neural
Networks, firstly the task of generating HDR map of a static scene using
differently exposed LDR images of the scene captured using conventional cameras
and secondly the task of finding an optimal tone mapping operator that would
give a better sco... | computer science |
1,408 | Deep Convolutional Neural Network Design Patterns | cs.LG | Recent research in the deep learning field has produced a plethora of new
architectures. At the same time, a growing number of groups are applying deep
learning to new applications. Some of these groups are likely to be composed of
inexperienced deep learning practitioners who are baffled by the dizzying array
of archi... | computer science |
1,409 | Fast Video Classification via Adaptive Cascading of Deep Models | cs.CV | Recent advances have enabled "oracle" classifiers that can classify across
many classes and input distributions with high accuracy without retraining.
However, these classifiers are relatively heavyweight, so that applying them to
classify video is costly. We show that day-to-day video exhibits highly skewed
class dist... | computer science |
1,410 | Quad-networks: unsupervised learning to rank for interest point
detection | cs.CV | Several machine learning tasks require to represent the data using only a
sparse set of interest points. An ideal detector is able to find the
corresponding interest points even if the data undergo a transformation typical
for a given domain. Since the task is of high practical interest in computer
vision, many hand-cr... | computer science |
1,411 | Deep Neural Networks - A Brief History | cs.NE | Introduction to deep neural networks and their history. | computer science |
1,412 | Theory II: Landscape of the Empirical Risk in Deep Learning | cs.LG | Previous theoretical work on deep learning and neural network optimization
tend to focus on avoiding saddle points and local minima. However, the
practical observation is that, at least in the case of the most successful Deep
Convolutional Neural Networks (DCNNs), practitioners can always increase the
network size to f... | computer science |
1,413 | Diabetic Retinopathy Detection via Deep Convolutional Networks for
Discriminative Localization and Visual Explanation | cs.CV | We proposed a deep learning method for interpretable diabetic retinopathy
(DR) detection. The visual-interpretable feature of the proposed method is
achieved by adding the regression activation map (RAM) after the global
averaging pooling layer of the convolutional networks (CNN). With RAM, the
proposed model can local... | computer science |
1,414 | DyVEDeep: Dynamic Variable Effort Deep Neural Networks | cs.NE | Deep Neural Networks (DNNs) have advanced the state-of-the-art in a variety
of machine learning tasks and are deployed in increasing numbers of products
and services. However, the computational requirements of training and
evaluating large-scale DNNs are growing at a much faster pace than the
capabilities of the underl... | computer science |
1,415 | Learning Important Features Through Propagating Activation Differences | cs.CV | The purported "black box"' nature of neural networks is a barrier to adoption
in applications where interpretability is essential. Here we present DeepLIFT
(Deep Learning Important FeaTures), a method for decomposing the output
prediction of a neural network on a specific input by backpropagating the
contributions of a... | computer science |
1,416 | Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on
Graphs | cs.CV | A number of problems can be formulated as prediction on graph-structured
data. In this work, we generalize the convolution operator from regular grids
to arbitrary graphs while avoiding the spectral domain, which allows us to
handle graphs of varying size and connectivity. To move beyond a simple
diffusion, filter weig... | computer science |
1,417 | Introspective Classification with Convolutional Nets | cs.CV | We propose introspective convolutional networks (ICN) that emphasize the
importance of having convolutional neural networks empowered with generative
capabilities. We employ a reclassification-by-synthesis algorithm to perform
training using a formulation stemmed from the Bayes theory. Our ICN tries to
iteratively: (1)... | computer science |
1,418 | Explaining How a Deep Neural Network Trained with End-to-End Learning
Steers a Car | cs.CV | As part of a complete software stack for autonomous driving, NVIDIA has
created a neural-network-based system, known as PilotNet, which outputs
steering angles given images of the road ahead. PilotNet is trained using road
images paired with the steering angles generated by a human driving a
data-collection car. It der... | computer science |
1,419 | Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery
for Pathologically-Proven Lung Cancer Detection | cs.NE | While lung cancer is the second most diagnosed form of cancer in men and
women, a sufficiently early diagnosis can be pivotal in patient survival rates.
Imaging-based, or radiomics-driven, detection methods have been developed to
aid diagnosticians, but largely rely on hand-crafted features which may not
fully encapsul... | computer science |
1,420 | Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain
Surgeon | cs.NE | How to develop slim and accurate deep neural networks has become crucial for
real- world applications, especially for those employed in embedded systems.
Though previous work along this research line has shown some promising results,
most existing methods either fail to significantly compress a well-trained deep
networ... | computer science |
1,421 | Matching neural paths: transfer from recognition to correspondence
search | cs.CV | Many machine learning tasks require finding per-part correspondences between
objects. In this work we focus on low-level correspondences - a highly
ambiguous matching problem. We propose to use a hierarchical semantic
representation of the objects, coming from a convolutional neural network, to
solve this ambiguity. Tr... | computer science |
1,422 | Deep Multi-instance Networks with Sparse Label Assignment for Whole
Mammogram Classification | cs.CV | Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labelin... | computer science |
1,423 | Accelerating Neural Architecture Search using Performance Prediction | cs.LG | Methods for neural network hyperparameter optimization and meta-modeling are
computationally expensive due to the need to train a large number of model
configurations. In this paper, we show that standard frequentist regression
models can predict the final performance of partially trained model
configurations using fea... | computer science |
1,424 | Exploring the Imposition of Synaptic Precision Restrictions For
Evolutionary Synthesis of Deep Neural Networks | cs.NE | A key contributing factor to incredible success of deep neural networks has
been the significant rise on massively parallel computing devices allowing
researchers to greatly increase the size and depth of deep neural networks,
leading to significant improvements in modeling accuracy. Although deeper,
larger, or complex... | computer science |
1,425 | Deep-learning-based data page classification for holographic memory | cs.CV | We propose a deep-learning-based classification of data pages used in
holographic memory. We numerically investigated the classification performance
of a conventional multi-layer perceptron (MLP) and a deep neural network, under
the condition that reconstructed page data are contaminated by some noise and
are randomly ... | computer science |
1,426 | Data-Driven Sparse Structure Selection for Deep Neural Networks | cs.CV | Deep convolutional neural networks have liberated its extraordinary power on
various tasks. However, it is still very challenging to deploy state-of-the-art
models into real-world applications due to their high computational complexity.
How can we design a compact and effective network without massive experiments
and e... | computer science |
1,427 | Like What You Like: Knowledge Distill via Neuron Selectivity Transfer | cs.CV | Despite deep neural networks have demonstrated extraordinary power in various
applications, their superior performances are at expense of high storage and
computational costs. Consequently, the acceleration and compression of neural
networks have attracted much attention recently. Knowledge Transfer (KT), which
aims at... | computer science |
1,428 | WRPN: Wide Reduced-Precision Networks | cs.CV | For computer vision applications, prior works have shown the efficacy of
reducing numeric precision of model parameters (network weights) in deep neural
networks. Activation maps, however, occupy a large memory footprint during both
the training and inference step when using mini-batches of inputs. One way to
reduce th... | computer science |
1,429 | NiftyNet: a deep-learning platform for medical imaging | cs.CV | Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implem... | computer science |
1,430 | DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule
Detection and Classification | cs.CV | In this work, we present a fully automated lung CT cancer diagnosis system,
DeepLung. DeepLung contains two parts, nodule detection and classification.
Considering the 3D nature of lung CT data, two 3D networks are designed for the
nodule detection and classification respectively. Specifically, a 3D Faster
R-CNN is des... | computer science |
1,431 | Improving image generative models with human interactions | cs.CV | GANs provide a framework for training generative models which mimic a data
distribution. However, in many cases we wish to train these generative models
to optimize some auxiliary objective function within the data it generates,
such as making more aesthetically pleasing images. In some cases, these
objective functions... | computer science |
1,432 | Searching for Activation Functions | cs.NE | The choice of activation functions in deep networks has a significant effect
on the training dynamics and task performance. Currently, the most successful
and widely-used activation function is the Rectified Linear Unit (ReLU).
Although various hand-designed alternatives to ReLU have been proposed, none
have managed to... | computer science |
1,433 | ResBinNet: Residual Binary Neural Network | cs.LG | Recent efforts on training light-weight binary neural networks offer
promising execution/memory efficiency. This paper introduces ResBinNet, which
is a composition of two interlinked methodologies aiming to address the slow
convergence speed and limited accuracy of binary convolutional neural networks.
The first method... | computer science |
1,434 | MarrNet: 3D Shape Reconstruction via 2.5D Sketches | cs.CV | 3D object reconstruction from a single image is a highly under-determined
problem, requiring strong prior knowledge of plausible 3D shapes. This
introduces challenges for learning-based approaches, as 3D object annotations
are scarce in real images. Previous work chose to train on synthetic data with
ground truth 3D in... | computer science |
1,435 | Improvements to context based self-supervised learning | cs.CV | We develop a set of methods to improve on the results of self-supervised
learning using context. We start with a baseline of patch based arrangement
context learning and go from there. Our methods address some overt problems
such as chromatic aberration as well as other potential problems such as
spatial skew and mid-l... | computer science |
1,436 | Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs | cs.CV | We propose a novel deep learning-based framework to tackle the challenge of
semantic segmentation of large-scale point clouds of millions of points. We
argue that the organization of 3D point clouds can be efficiently captured by a
structure called superpoint graph (SPG), derived from a partition of the
scanned scene i... | computer science |
1,437 | CNNs are Globally Optimal Given Multi-Layer Support | cs.LG | Stochastic Gradient Descent (SGD) is the central workhorse for training
modern CNNs. Although giving impressive empirical performance it can be slow to
converge. In this paper we explore a novel strategy for training a CNN using an
alternation strategy that offers substantial speedups during training. We make
the follo... | computer science |
1,438 | An Artificial Neural Network Architecture Based on Context
Transformations in Cortical Minicolumns | cs.CV | Cortical minicolumns are considered a model of cortical organization. Their
function is still a source of research and not reflected properly in modern
architecture of nets in algorithms of Artificial Intelligence. We assume its
function and describe it in this article. Furthermore, we show how this
proposal allows to ... | computer science |
1,439 | Stochastic Downsampling for Cost-Adjustable Inference and Improved
Regularization in Convolutional Networks | cs.LG | It is desirable to train convolutional networks (CNNs) to run more
efficiently during inference. In many cases however, the computational budget
that the system has for inference cannot be known beforehand during training,
or the inference budget is dependent on the changing real-time resource
availability. Thus, it is... | computer science |
1,440 | GraphVAE: Towards Generation of Small Graphs Using Variational
Autoencoders | cs.LG | Deep learning on graphs has become a popular research topic with many
applications. However, past work has concentrated on learning graph embedding
tasks, which is in contrast with advances in generative models for images and
text. Is it possible to transfer this progress to the domain of graphs? We
propose to sidestep... | computer science |
1,441 | WRPN & Apprentice: Methods for Training and Inference using
Low-Precision Numerics | cs.CV | Today's high performance deep learning architectures involve large models
with numerous parameters. Low precision numerics has emerged as a popular
technique to reduce both the compute and memory requirements of these large
models. However, lowering precision often leads to accuracy degradation. We
describe three schem... | computer science |
1,442 | A Distance Oriented Kalman Filter Particle Swarm Optimizer Applied to
Multi-Modality Image Registration | cs.NE | In this paper we describe improvements to the particle swarm optimizer (PSO)
made by inclusion of an unscented Kalman filter to guide particle motion. We
demonstrate the effectiveness of the unscented Kalman filter PSO by comparing
it with the original PSO algorithm and its variants designed to improve
performance. The... | computer science |
1,443 | Inferring Robot Task Plans from Human Team Meetings: A Generative
Modeling Approach with Logic-Based Prior | cs.AI | We aim to reduce the burden of programming and deploying autonomous systems
to work in concert with people in time-critical domains, such as military field
operations and disaster response. Deployment plans for these operations are
frequently negotiated on-the-fly by teams of human planners. A human operator
then trans... | computer science |
1,444 | KSU KDD: Word Sense Induction by Clustering in Topic Space | cs.CL | We describe our language-independent unsupervised word sense induction
system. This system only uses topic features to cluster different word senses
in their global context topic space. Using unlabeled data, this system trains a
latent Dirichlet allocation (LDA) topic model then uses it to infer the topics
distribution... | computer science |
1,445 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | cs.AI | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question a... | computer science |
1,446 | Memory Networks | cs.AI | We describe a new class of learning models called memory networks. Memory
networks reason with inference components combined with a long-term memory
component; they learn how to use these jointly. The long-term memory can be
read and written to, with the goal of using it for prediction. We investigate
these models in t... | computer science |
1,447 | Evaluation Evaluation a Monte Carlo study | cs.AI | Over the last decade there has been increasing concern about the biases
embodied in traditional evaluation methods for Natural Language
Processing/Learning, particularly methods borrowed from Information Retrieval.
Without knowledge of the Bias and Prevalence of the contingency being tested,
or equivalently the expecta... | computer science |
1,448 | Traversing Knowledge Graphs in Vector Space | cs.CL | Path queries on a knowledge graph can be used to answer compositional
questions such as "What languages are spoken by people living in Lisbon?".
However, knowledge graphs often have missing facts (edges) which disrupts path
queries. Recent models for knowledge base completion impute missing facts by
embedding knowledge... | computer science |
1,449 | Humor in Collective Discourse: Unsupervised Funniness Detection in the
New Yorker Cartoon Caption Contest | cs.CL | The New Yorker publishes a weekly captionless cartoon. More than 5,000
readers submit captions for it. The editors select three of them and ask the
readers to pick the funniest one. We describe an experiment that compares a
dozen automatic methods for selecting the funniest caption. We show that
negative sentiment, hum... | computer science |
1,450 | Machine Learning Sentiment Prediction based on Hybrid Document
Representation | cs.CL | Automated sentiment analysis and opinion mining is a complex process
concerning the extraction of useful subjective information from text. The
explosion of user generated content on the Web, especially the fact that
millions of users, on a daily basis, express their opinions on products and
services to blogs, wikis, so... | computer science |
1,451 | Mapping distributional to model-theoretic semantic spaces: a baseline | cs.CL | Word embeddings have been shown to be useful across state-of-the-art systems
in many natural language processing tasks, ranging from question answering
systems to dependency parsing. (Herbelot and Vecchi, 2015) explored word
embeddings and their utility for modeling language semantics. In particular,
they presented an ... | computer science |
1,452 | Cutting-off Redundant Repeating Generations for Neural Abstractive
Summarization | cs.CL | This paper tackles the reduction of redundant repeating generation that is
often observed in RNN-based encoder-decoder models. Our basic idea is to
jointly estimate the upper-bound frequency of each target vocabulary in the
encoder and control the output words based on the estimation in the decoder.
Our method shows si... | computer science |
1,453 | Modeling Semantic Expectation: Using Script Knowledge for Referent
Prediction | cs.CL | Recent research in psycholinguistics has provided increasing evidence that
humans predict upcoming content. Prediction also affects perception and might
be a key to robustness in human language processing. In this paper, we
investigate the factors that affect human prediction by building a
computational model that can ... | computer science |
1,454 | Multi-Task Learning of Keyphrase Boundary Classification | cs.CL | Keyphrase boundary classification (KBC) is the task of detecting keyphrases
in scientific articles and labelling them with respect to predefined types.
Although important in practice, this task is so far underexplored, partly due
to the lack of labelled data. To overcome this, we explore several auxiliary
tasks, includ... | computer science |
1,455 | An Automated Text Categorization Framework based on Hyperparameter
Optimization | cs.CL | A great variety of text tasks such as topic or spam identification, user
profiling, and sentiment analysis can be posed as a supervised learning problem
and tackle using a text classifier. A text classifier consists of several
subprocesses, some of them are general enough to be applied to any supervised
learning proble... | computer science |
1,456 | SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations
from Scientific Publications | cs.CL | We describe the SemEval task of extracting keyphrases and relations between
them from scientific documents, which is crucial for understanding which
publications describe which processes, tasks and materials. Although this was a
new task, we had a total of 26 submissions across 3 evaluation scenarios. We
expect the tas... | computer science |
1,457 | Accelerating Innovation Through Analogy Mining | cs.CL | The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge ... | computer science |
1,458 | Understanding State Preferences With Text As Data: Introducing the UN
General Debate Corpus | cs.CL | Every year at the United Nations, member states deliver statements during the
General Debate discussing major issues in world politics. These speeches
provide invaluable information on governments' perspectives and preferences on
a wide range of issues, but have largely been overlooked in the study of
international pol... | computer science |
1,459 | Detecting Policy Preferences and Dynamics in the UN General Debate with
Neural Word Embeddings | cs.CL | Foreign policy analysis has been struggling to find ways to measure policy
preferences and paradigm shifts in international political systems. This paper
presents a novel, potential solution to this challenge, through the application
of a neural word embedding (Word2vec) model on a dataset featuring speeches by
heads o... | computer science |
1,460 | Crowdsourcing Multiple Choice Science Questions | cs.HC | We present a novel method for obtaining high-quality, domain-targeted
multiple choice questions from crowd workers. Generating these questions can be
difficult without trading away originality, relevance or diversity in the
answer options. Our method addresses these problems by leveraging a large
corpus of domain-speci... | computer science |
1,461 | PubMed 200k RCT: a Dataset for Sequential Sentence Classification in
Medical Abstracts | cs.CL | We present PubMed 200k RCT, a new dataset based on PubMed for sequential
sentence classification. The dataset consists of approximately 200,000
abstracts of randomized controlled trials, totaling 2.3 million sentences. Each
sentence of each abstract is labeled with their role in the abstract using one
of the following ... | computer science |
1,462 | Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case
Study | cs.CL | Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram
features when learning from textual data. They are also compatible with the use
of word embeddings so that word similarities can be accounted for. While the
original any-gram kernels are implemented on top of tree kernels, we propose a
new approa... | computer science |
1,463 | High Order Recurrent Neural Networks for Acoustic Modelling | cs.CL | Vanishing long-term gradients are a major issue in training standard
recurrent neural networks (RNNs), which can be alleviated by long short-term
memory (LSTM) models with memory cells. However, the extra parameters
associated with the memory cells mean an LSTM layer has four times as many
parameters as an RNN with the... | computer science |
1,464 | The emergent algebraic structure of RNNs and embeddings in NLP | cs.CL | We examine the algebraic and geometric properties of a uni-directional GRU
and word embeddings trained end-to-end on a text classification task. A
hyperparameter search over word embedding dimension, GRU hidden dimension, and
a linear combination of the GRU outputs is performed. We conclude that words
naturally embed t... | computer science |
1,465 | Bank distress in the news: Describing events through deep learning | cs.CL | While many models are purposed for detecting the occurrence of significant
events in financial systems, the task of providing qualitative detail on the
developments is not usually as well automated. We present a deep learning
approach for detecting relevant discussion in text and extracting natural
language description... | computer science |
1,466 | A modular architecture for transparent computation in Recurrent Neural
Networks | cs.NE | Computation is classically studied in terms of automata, formal languages and
algorithms; yet, the relation between neural dynamics and symbolic
representations and operations is still unclear in traditional eliminative
connectionism. Therefore, we suggest a unique perspective on this central
issue, to which we would l... | computer science |
1,467 | graph2vec: Learning Distributed Representations of Graphs | cs.AI | Recent works on representation learning for graph structured data
predominantly focus on learning distributed representations of graph
substructures such as nodes and subgraphs. However, many graph analytics tasks
such as graph classification and clustering require representing entire graphs
as fixed length feature vec... | computer science |
1,468 | Ask Your Neurons: A Neural-based Approach to Answering Questions about
Images | cs.CV | We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Neural-Image-QA, an end-to-end
formulation to this problem for which all parts are trained jointly. In
contrast to previous e... | computer science |
1,469 | Attentive Explanations: Justifying Decisions and Pointing to the
Evidence | cs.CV | Deep models are the defacto standard in visual decision models due to their
impressive performance on a wide array of visual tasks. However, they are
frequently seen as opaque and are unable to explain their decisions. In
contrast, humans can justify their decisions with natural language and point to
the evidence in th... | computer science |
1,470 | A Joint Speaker-Listener-Reinforcer Model for Referring Expressions | cs.CV | Referring expressions are natural language constructions used to identify
particular objects within a scene. In this paper, we propose a unified
framework for the tasks of referring expression comprehension and generation.
Our model is composed of three modules: speaker, listener, and reinforcer. The
speaker generates ... | computer science |
1,471 | Learning a Recurrent Visual Representation for Image Caption Generation | cs.CV | In this paper we explore the bi-directional mapping between images and their
sentence-based descriptions. We propose learning this mapping using a recurrent
neural network. Unlike previous approaches that map both sentences and images
to a common embedding, we enable the generation of novel sentences given an
image. Us... | computer science |
1,472 | A Survey of Current Datasets for Vision and Language Research | cs.CL | Integrating vision and language has long been a dream in work on artificial
intelligence (AI). In the past two years, we have witnessed an explosion of
work that brings together vision and language from images to videos and beyond.
The available corpora have played a crucial role in advancing this area of
research. In ... | computer science |
1,473 | Talking about the Moving Image: A Declarative Model for Image Schema
Based Embodied Perception Grounding and Language Generation | cs.AI | We present a general theory and corresponding declarative model for the
embodied grounding and natural language based analytical summarisation of
dynamic visuo-spatial imagery. The declarative model ---ecompassing
spatio-linguistic abstractions, image schemas, and a spatio-temporal feature
based language generator--- i... | computer science |
1,474 | Symbol Emergence in Robotics: A Survey | cs.AI | Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, man... | computer science |
1,475 | From Images to Sentences through Scene Description Graphs using
Commonsense Reasoning and Knowledge | cs.CV | In this paper we propose the construction of linguistic descriptions of
images. This is achieved through the extraction of scene description graphs
(SDGs) from visual scenes using an automatically constructed knowledge base.
SDGs are constructed using both vision and reasoning. Specifically, commonsense
reasoning is ap... | computer science |
1,476 | Generating Natural Questions About an Image | cs.CL | There has been an explosion of work in the vision & language community during
the past few years from image captioning to video transcription, and answering
questions about images. These tasks have focused on literal descriptions of the
image. To move beyond the literal, we choose to explore how questions about an
imag... | computer science |
1,477 | Do You See What I Mean? Visual Resolution of Linguistic Ambiguities | cs.CV | Understanding language goes hand in hand with the ability to integrate
complex contextual information obtained via perception. In this work, we
present a novel task for grounded language understanding: disambiguating a
sentence given a visual scene which depicts one of the possible interpretations
of that sentence. To ... | computer science |
1,478 | Generating Visual Explanations | cs.CV | Clearly explaining a rationale for a classification decision to an end-user
can be as important as the decision itself. Existing approaches for deep visual
recognition are generally opaque and do not output any justification text;
contemporary vision-language models can describe image content but fail to take
into acco... | computer science |
1,479 | Ask Your Neurons: A Deep Learning Approach to Visual Question Answering | cs.CV | We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Ask Your Neurons, a scalable, jointly
trained, end-to-end formulation to this problem.
In contrast to previous efforts, we ... | computer science |
1,480 | Multimodal Compact Bilinear Pooling for Visual Question Answering and
Visual Grounding | cs.CV | Modeling textual or visual information with vector representations trained
from large language or visual datasets has been successfully explored in recent
years. However, tasks such as visual question answering require combining these
vector representations with each other. Approaches to multimodal pooling
include elem... | computer science |
1,481 | Visual Question: Predicting If a Crowd Will Agree on the Answer | cs.AI | Visual question answering (VQA) systems are emerging from a desire to empower
users to ask any natural language question about visual content and receive a
valid answer in response. However, close examination of the VQA problem reveals
an unavoidable, entangled problem that multiple humans may or may not always
agree o... | computer science |
1,482 | Context Aware Nonnegative Matrix Factorization Clustering | cs.CV | In this article we propose a method to refine the clustering results obtained
with the nonnegative matrix factorization (NMF) technique, imposing consistency
constraints on the final labeling of the data. The research community focused
its effort on the initialization and on the optimization part of this method,
withou... | computer science |
1,483 | Graph-Structured Representations for Visual Question Answering | cs.CV | This paper proposes to improve visual question answering (VQA) with
structured representations of both scene contents and questions. A key
challenge in VQA is to require joint reasoning over the visual and text
domains. The predominant CNN/LSTM-based approach to VQA is limited by
monolithic vector representations that ... | computer science |
1,484 | Visual Question Answering: Datasets, Algorithms, and Future Challenges | cs.CV | Visual Question Answering (VQA) is a recent problem in computer vision and
natural language processing that has garnered a large amount of interest from
the deep learning, computer vision, and natural language processing
communities. In VQA, an algorithm needs to answer text-based questions about
images. Since the rele... | computer science |
1,485 | Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models | cs.AI | Neural sequence models are widely used to model time-series data in many
fields. Equally ubiquitous is the usage of beam search (BS) as an approximate
inference algorithm to decode output sequences from these models. BS explores
the search space in a greedy left-right fashion retaining only the top-$B$
candidates -- re... | computer science |
1,486 | Proposing Plausible Answers for Open-ended Visual Question Answering | cs.CL | Answering open-ended questions is an essential capability for any intelligent
agent. One of the most interesting recent open-ended question answering
challenges is Visual Question Answering (VQA) which attempts to evaluate a
system's visual understanding through its answers to natural language questions
about images. T... | computer science |
1,487 | Zero-Shot Visual Question Answering | cs.CV | Part of the appeal of Visual Question Answering (VQA) is its promise to
answer new questions about previously unseen images. Most current methods
demand training questions that illustrate every possible concept, and will
therefore never achieve this capability, since the volume of required training
data would be prohib... | computer science |
1,488 | Image-Grounded Conversations: Multimodal Context for Natural Question
and Response Generation | cs.CL | The popularity of image sharing on social media and the engagement it creates
between users reflects the important role that visual context plays in everyday
conversations. We present a novel task, Image-Grounded Conversations (IGC), in
which natural-sounding conversations are generated about a shared image. To
benchma... | computer science |
1,489 | Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers
from Vision | cs.CL | People can refer to quantities in a visual scene by using either exact
cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few,
most, all). In humans, these two processes underlie fairly different cognitive
and neural mechanisms. Inspired by this evidence, the present study proposes
two models for le... | computer science |
1,490 | An Analysis of Visual Question Answering Algorithms | cs.CV | In visual question answering (VQA), an algorithm must answer text-based
questions about images. While multiple datasets for VQA have been created since
late 2014, they all have flaws in both their content and the way algorithms are
evaluated on them. As a result, evaluation scores are inflated and
predominantly determi... | computer science |
1,491 | Speaking the Same Language: Matching Machine to Human Captions by
Adversarial Training | cs.CV | While strong progress has been made in image captioning over the last years,
machine and human captions are still quite distinct. A closer look reveals that
this is due to the deficiencies in the generated word distribution, vocabulary
size, and strong bias in the generators towards frequent captions. Furthermore,
huma... | computer science |
1,492 | It Takes Two to Tango: Towards Theory of AI's Mind | cs.CV | Theory of Mind is the ability to attribute mental states (beliefs, intents,
knowledge, perspectives, etc.) to others and recognize that these mental states
may differ from one's own. Theory of Mind is critical to effective
communication and to teams demonstrating higher collective performance. To
effectively leverage t... | computer science |
1,493 | Pay Attention to Those Sets! Learning Quantification from Images | cs.CL | Major advances have recently been made in merging language and vision
representations. But most tasks considered so far have confined themselves to
the processing of objects and lexicalised relations amongst objects (content
words). We know, however, that humans (even pre-school children) can abstract
over raw data to ... | computer science |
1,494 | ShapeWorld - A new test methodology for multimodal language
understanding | cs.CL | We introduce a novel framework for evaluating multimodal deep learning models
with respect to their language understanding and generalization abilities. In
this approach, artificial data is automatically generated according to the
experimenter's specifications. The content of the data, both during training
and evaluati... | computer science |
1,495 | Multi-Task Video Captioning with Video and Entailment Generation | cs.CL | Video captioning, the task of describing the content of a video, has seen
some promising improvements in recent years with sequence-to-sequence models,
but accurately learning the temporal and logical dynamics involved in the task
still remains a challenge, especially given the lack of sufficient annotated
data. We imp... | computer science |
1,496 | Punny Captions: Witty Wordplay in Image Descriptions | cs.CL | Wit is a quintessential form of rich inter-human interaction, and is often
grounded in a specific situation (e.g., a comment in response to an event). In
this work, we attempt to build computational models that can produce witty
descriptions for a given image. Inspired by a cognitive account of humor
appreciation, we e... | computer science |
1,497 | Survey of Visual Question Answering: Datasets and Techniques | cs.CL | Visual question answering (or VQA) is a new and exciting problem that
combines natural language processing and computer vision techniques. We present
a survey of the various datasets and models that have been used to tackle this
task. The first part of the survey details the various datasets for VQA and
compares them a... | computer science |
1,498 | Teaching Machines to Describe Images via Natural Language Feedback | cs.CL | Robots will eventually be part of every household. It is thus critical to
enable algorithms to learn from and be guided by non-expert users. In this
paper, we bring a human in the loop, and enable a human teacher to give
feedback to a learning agent in the form of natural language. We argue that a
descriptive sentence ... | computer science |
1,499 | Best of Both Worlds: Transferring Knowledge from Discriminative Learning
to a Generative Visual Dialog Model | cs.CV | We present a novel training framework for neural sequence models,
particularly for grounded dialog generation. The standard training paradigm for
these models is maximum likelihood estimation (MLE), or minimizing the
cross-entropy of the human responses. Across a variety of domains, a recurring
problem with MLE trained... | computer science |
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