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1,900 | Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | cs.CV | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-networ... | computer science |
1,901 | Combining Language and Vision with a Multimodal Skip-gram Model | cs.CL | We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual
information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM)
build vector-based word representations by learning to predict linguistic
contexts in text corpora. However, for a restricted set of words, the models
are also exposed t... | computer science |
1,902 | Learning like a Child: Fast Novel Visual Concept Learning from Sentence
Descriptions of Images | cs.CV | In this paper, we address the task of learning novel visual concepts, and
their interactions with other concepts, from a few images with sentence
descriptions. Using linguistic context and visual features, our method is able
to efficiently hypothesize the semantic meaning of new words and add them to
its word dictionar... | computer science |
1,903 | Scheduled Sampling for Sequence Prediction with Recurrent Neural
Networks | cs.LG | Recurrent Neural Networks can be trained to produce sequences of tokens given
some input, as exemplified by recent results in machine translation and image
captioning. The current approach to training them consists of maximizing the
likelihood of each token in the sequence given the current (recurrent) state
and the pr... | computer science |
1,904 | Generation and Comprehension of Unambiguous Object Descriptions | cs.CV | We propose a method that can generate an unambiguous description (known as a
referring expression) of a specific object or region in an image, and which can
also comprehend or interpret such an expression to infer which object is being
described. We show that our method outperforms previous methods that generate
descri... | computer science |
1,905 | Grounding of Textual Phrases in Images by Reconstruction | cs.CV | Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual
content is a challenging problem with many applications for human-computer
interaction and image-text reference resolution. Few datasets provide the
ground truth spatial localization of phrases, thus it is desirable to learn
from data with no or... | computer science |
1,906 | Sherlock: Scalable Fact Learning in Images | cs.CV | We study scalable and uniform understanding of facts in images. Existing
visual recognition systems are typically modeled differently for each fact type
such as objects, actions, and interactions. We propose a setting where all
these facts can be modeled simultaneously with a capacity to understand
unbounded number of ... | computer science |
1,907 | Yin and Yang: Balancing and Answering Binary Visual Questions | cs.CL | The complex compositional structure of language makes problems at the
intersection of vision and language challenging. But language also provides a
strong prior that can result in good superficial performance, without the
underlying models truly understanding the visual content. This can hinder
progress in pushing stat... | computer science |
1,908 | Image Question Answering using Convolutional Neural Network with Dynamic
Parameter Prediction | cs.CV | We tackle image question answering (ImageQA) problem by learning a
convolutional neural network (CNN) with a dynamic parameter layer whose weights
are determined adaptively based on questions. For the adaptive parameter
prediction, we employ a separate parameter prediction network, which consists
of gated recurrent uni... | computer science |
1,909 | Learning Deep Structure-Preserving Image-Text Embeddings | cs.CV | This paper proposes a method for learning joint embeddings of images and text
using a two-branch neural network with multiple layers of linear projections
followed by nonlinearities. The network is trained using a large margin
objective that combines cross-view ranking constraints with within-view
neighborhood structur... | computer science |
1,910 | Order-Embeddings of Images and Language | cs.LG | Hypernymy, textual entailment, and image captioning can be seen as special
cases of a single visual-semantic hierarchy over words, sentences, and images.
In this paper we advocate for explicitly modeling the partial order structure
of this hierarchy. Towards this goal, we introduce a general method for
learning ordered... | computer science |
1,911 | Zero-Shot Event Detection by Multimodal Distributional Semantic
Embedding of Videos | cs.CV | We propose a new zero-shot Event Detection method by Multi-modal
Distributional Semantic embedding of videos. Our model embeds object and action
concepts as well as other available modalities from videos into a
distributional semantic space. To our knowledge, this is the first Zero-Shot
event detection model that is bu... | computer science |
1,912 | We Are Humor Beings: Understanding and Predicting Visual Humor | cs.CV | Humor is an integral part of human lives. Despite being tremendously
impactful, it is perhaps surprising that we do not have a detailed
understanding of humor yet. As interactions between humans and AI systems
increase, it is imperative that these systems are taught to understand
subtleties of human expressions such as... | computer science |
1,913 | Write a Classifier: Predicting Visual Classifiers from Unstructured Text | cs.CV | People typically learn through exposure to visual concepts associated with
linguistic descriptions. For instance, teaching visual object categories to
children is often accompanied by descriptions in text or speech. In a machine
learning context, these observations motivates us to ask whether this learning
process coul... | computer science |
1,914 | Deep Learning Applied to Image and Text Matching | cs.LG | The ability to describe images with natural language sentences is the
hallmark for image and language understanding. Such a system has wide ranging
applications such as annotating images and using natural sentences to search
for images.In this project we focus on the task of bidirectional image
retrieval: such asystem ... | computer science |
1,915 | Generate Image Descriptions based on Deep RNN and Memory Cells for
Images Features | cs.CV | Generating natural language descriptions for images is a challenging task.
The traditional way is to use the convolutional neural network (CNN) to extract
image features, followed by recurrent neural network (RNN) to generate
sentences. In this paper, we present a new model that added memory cells to
gate the feeding o... | computer science |
1,916 | Audio Visual Emotion Recognition with Temporal Alignment and Perception
Attention | cs.CV | This paper focuses on two key problems for audio-visual emotion recognition
in the video. One is the audio and visual streams temporal alignment for
feature level fusion. The other one is locating and re-weighting the perception
attentions in the whole audio-visual stream for better recognition. The Long
Short Term Mem... | computer science |
1,917 | Towards Multi-Agent Communication-Based Language Learning | cs.CL | We propose an interactive multimodal framework for language learning. Instead
of being passively exposed to large amounts of natural text, our learners
(implemented as feed-forward neural networks) engage in cooperative referential
games starting from a tabula rasa setup, and thus develop their own language
from the ne... | computer science |
1,918 | Review Networks for Caption Generation | cs.LG | We propose a novel extension of the encoder-decoder framework, called a
review network. The review network is generic and can enhance any existing
encoder- decoder model: in this paper, we consider RNN decoders with both CNN
and RNN encoders. The review network performs a number of review steps with
attention mechanism... | computer science |
1,919 | Stacking With Auxiliary Features | cs.CL | Ensembling methods are well known for improving prediction accuracy. However,
they are limited in the sense that they cannot discriminate among component
models effectively. In this paper, we propose stacking with auxiliary features
that learns to fuse relevant information from multiple systems to improve
performance. ... | computer science |
1,920 | Attention Correctness in Neural Image Captioning | cs.CV | Attention mechanisms have recently been introduced in deep learning for
various tasks in natural language processing and computer vision. But despite
their popularity, the "correctness" of the implicitly-learned attention maps
has only been assessed qualitatively by visualization of several examples. In
this paper we f... | computer science |
1,921 | Question Relevance in VQA: Identifying Non-Visual And False-Premise
Questions | cs.CV | Visual Question Answering (VQA) is the task of answering natural-language
questions about images. We introduce the novel problem of determining the
relevance of questions to images in VQA. Current VQA models do not reason about
whether a question is even related to the given image (e.g. What is the capital
of Argentina... | computer science |
1,922 | Learning Concept Taxonomies from Multi-modal Data | cs.CL | We study the problem of automatically building hypernym taxonomies from
textual and visual data. Previous works in taxonomy induction generally ignore
the increasingly prominent visual data, which encode important perceptual
semantics. Instead, we propose a probabilistic model for taxonomy induction by
jointly leveragi... | computer science |
1,923 | The KIT Motion-Language Dataset | cs.RO | Linking human motion and natural language is of great interest for the
generation of semantic representations of human activities as well as for the
generation of robot activities based on natural language input. However, while
there have been years of research in this area, no standardized and openly
available dataset... | computer science |
1,924 | LipNet: End-to-End Sentence-level Lipreading | cs.LG | Lipreading is the task of decoding text from the movement of a speaker's
mouth. Traditional approaches separated the problem into two stages: designing
or learning visual features, and prediction. More recent deep lipreading
approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman,
2016a). However, exi... | computer science |
1,925 | Audio Visual Speech Recognition using Deep Recurrent Neural Networks | cs.CV | In this work, we propose a training algorithm for an audio-visual automatic
speech recognition (AV-ASR) system using deep recurrent neural network
(RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal
Classification (CTC) objective function. The frame labels obtained from the
acoustic model are ... | computer science |
1,926 | Statistical Learning for OCR Text Correction | cs.CV | The accuracy of Optical Character Recognition (OCR) is crucial to the success
of subsequent applications used in text analyzing pipeline. Recent models of
OCR post-processing significantly improve the quality of OCR-generated text,
but are still prone to suggest correction candidates from limited observations
while ins... | computer science |
1,927 | Semantic Compositional Networks for Visual Captioning | cs.CV | A Semantic Compositional Network (SCN) is developed for image captioning, in
which semantic concepts (i.e., tags) are detected from the image, and the
probability of each tag is used to compose the parameters in a long short-term
memory (LSTM) network. The SCN extends each weight matrix of the LSTM to an
ensemble of ta... | computer science |
1,928 | Training and Evaluating Multimodal Word Embeddings with Large-scale Web
Annotated Images | cs.LG | In this paper, we focus on training and evaluating effective word embeddings
with both text and visual information. More specifically, we introduce a
large-scale dataset with 300 million sentences describing over 40 million
images crawled and downloaded from publicly available Pins (i.e. an image with
sentence descript... | computer science |
1,929 | Deep Learning the Indus Script | cs.CV | Standardized corpora of undeciphered scripts, a necessary starting point for
computational epigraphy, requires laborious human effort for their preparation
from raw archaeological records. Automating this process through machine
learning algorithms can be of significant aid to epigraphical research. Here,
we take the f... | computer science |
1,930 | Learning Robust Visual-Semantic Embeddings | cs.CV | Many of the existing methods for learning joint embedding of images and text
use only supervised information from paired images and its textual attributes.
Taking advantage of the recent success of unsupervised learning in deep neural
networks, we propose an end-to-end learning framework that is able to extract
more ro... | computer science |
1,931 | Inferring and Executing Programs for Visual Reasoning | cs.CV | Existing methods for visual reasoning attempt to directly map inputs to
outputs using black-box architectures without explicitly modeling the
underlying reasoning processes. As a result, these black-box models often learn
to exploit biases in the data rather than learning to perform visual reasoning.
Inspired by module... | computer science |
1,932 | Better Text Understanding Through Image-To-Text Transfer | cs.CL | Generic text embeddings are successfully used in a variety of tasks. However,
they are often learnt by capturing the co-occurrence structure from pure text
corpora, resulting in limitations of their ability to generalize. In this
paper, we explore models that incorporate visual information into the text
representation.... | computer science |
1,933 | Emergence of Language with Multi-agent Games: Learning to Communicate
with Sequences of Symbols | cs.LG | Learning to communicate through interaction, rather than relying on explicit
supervision, is often considered a prerequisite for developing a general AI. We
study a setting where two agents engage in playing a referential game and, from
scratch, develop a communication protocol necessary to succeed in this game.
Unlike... | computer science |
1,934 | Modulating early visual processing by language | cs.CV | It is commonly assumed that language refers to high-level visual concepts
while leaving low-level visual processing unaffected. This view dominates the
current literature in computational models for language-vision tasks, where
visual and linguistic input are mostly processed independently before being
fused into a sin... | computer science |
1,935 | VSE++: Improving Visual-Semantic Embeddings with Hard Negatives | cs.LG | We present a new technique for learning visual-semantic embeddings for
cross-modal retrieval. Inspired by the use of hard negatives in structured
prediction, and ranking loss functions used in retrieval, we introduce a simple
change to common loss functions used to learn multi-modal embeddings. That,
combined with fine... | computer science |
1,936 | VQS: Linking Segmentations to Questions and Answers for Supervised
Attention in VQA and Question-Focused Semantic Segmentation | cs.CV | 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 ... | computer science |
1,937 | Self-Guiding Multimodal LSTM - when we do not have a perfect training
dataset for image captioning | cs.CV | In this paper, a self-guiding multimodal LSTM (sg-LSTM) image captioning
model is proposed to handle uncontrolled imbalanced real-world image-sentence
dataset. We collect FlickrNYC dataset from Flickr as our testbed with 306,165
images and the original text descriptions uploaded by the users are utilized as
the ground ... | computer science |
1,938 | Label Embedding Network: Learning Label Representation for Soft Training
of Deep Networks | cs.LG | We propose a method, called Label Embedding Network, which can learn label
representation (label embedding) during the training process of deep networks.
With the proposed method, the label embedding is adaptively and automatically
learned through back propagation. The original one-hot represented loss
function is conv... | computer science |
1,939 | Language-Based Image Editing with Recurrent Attentive Models | cs.CV | We investigate the problem of Language-Based Image Editing (LBIE) in this
work. Given a source image and a natural language description, we want to
generate a target image by editing the source im- age based on the description.
We propose a generic modeling framework for two sub-tasks of LBIE:
language-based image segm... | computer science |
1,940 | Learning by Asking Questions | cs.CV | We introduce an interactive learning framework for the development and
testing of intelligent visual systems, called learning-by-asking (LBA). We
explore LBA in context of the Visual Question Answering (VQA) task. LBA differs
from standard VQA training in that most questions are not observed during
training time, and t... | computer science |
1,941 | Learning Modality-Invariant Representations for Speech and Images | cs.LG | In this paper, we explore the unsupervised learning of a semantic embedding
space for co-occurring sensory inputs. Specifically, we focus on the task of
learning a semantic vector space for both spoken and handwritten digits using
the TIDIGITs and MNIST datasets. Current techniques encode image and
audio/textual inputs... | computer science |
1,942 | Synthesizing Novel Pairs of Image and Text | cs.CV | Generating novel pairs of image and text is a problem that combines computer
vision and natural language processing. In this paper, we present strategies
for generating novel image and caption pairs based on existing captioning
datasets. The model takes advantage of recent advances in generative
adversarial networks an... | computer science |
1,943 | LSTM stack-based Neural Multi-sequence Alignment TeCHnique (NeuMATCH) | cs.CV | The alignment of heterogeneous sequential data (video to text) is an
important and challenging problem. Standard techniques for such alignment,
including Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs),
suffer from inherent drawbacks. Mainly, the Markov assumption implies that,
given the immediate past,... | computer science |
1,944 | Machine Learning Markets | cs.AI | Prediction markets show considerable promise for developing flexible
mechanisms for machine learning. Here, machine learning markets for
multivariate systems are defined, and a utility-based framework is established
for their analysis. This differs from the usual approach of defining static
betting functions. It is sho... | computer science |
1,945 | Quantum Memristors in Quantum Photonics | cs.AI | We propose a method to build quantum memristors in quantum photonic
platforms. We firstly design an effective beam splitter, which is tunable in
real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam
splitters and a tunable retarder, which allows us to control its reflectivity.
Then, we show that th... | computer science |
1,946 | Computationally Efficient Target Classification in Multispectral Image
Data with Deep Neural Networks | cs.CV | Detecting and classifying targets in video streams from surveillance cameras
is a cumbersome, error-prone and expensive task. Often, the incurred costs are
prohibitive for real-time monitoring. This leads to data being stored locally
or transmitted to a central storage site for post-incident examination. The
required c... | computer science |
1,947 | SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning
and Control | cs.RO | In this work, we present an approach to deep visuomotor control using
structured deep dynamics models. Our deep dynamics model, a variant of
SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an
encoder-decoder structure. Unlike prior work, our dynamics model is structured:
given an input scen... | computer science |
1,948 | Fuzzy-Based Dialectical Non-Supervised Image Classification and
Clustering | cs.CV | The materialist dialectical method is a philosophical investigative method to
analyze aspects of reality. These aspects are viewed as complex processes
composed by basic units named poles, which interact with each other. Dialectics
has experienced considerable progress in the 19th century, with Hegel's
dialectics and, ... | computer science |
1,949 | Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance
for Action Classification and Detection | cs.CV | General human action recognition requires understanding of various visual
cues. In this paper, we propose a network architecture that computes and
integrates the most important visual cues for action recognition: pose, motion,
and the raw images. For the integration, we introduce a Markov chain model
which adds cues su... | computer science |
1,950 | A semi-supervised fuzzy GrowCut algorithm to segment and classify
regions of interest of mammographic images | cs.CV | According to the World Health Organization, breast cancer is the most common
form of cancer in women. It is the second leading cause of death among women
round the world, becoming the most fatal form of cancer. Mammographic image
segmentation is a fundamental task to support image analysis and diagnosis,
taking into ac... | computer science |
1,951 | AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem | stat.ML | This paper presents an improvement to model learning when using multi-class
LogitBoost for classification. Motivated by the statistical view, LogitBoost
can be seen as additive tree regression. Two important factors in this setting
are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the
dense Hess... | computer science |
1,952 | Variational Gaussian Process Dynamical Systems | stat.ML | High dimensional time series are endemic in applications of machine learning
such as robotics (sensor data), computational biology (gene expression data),
vision (video sequences) and graphics (motion capture data). Practical
nonlinear probabilistic approaches to this data are required. In this paper we
introduce the v... | computer science |
1,953 | Managing sparsity, time, and quality of inference in topic models | stat.ML | Inference is an integral part of probabilistic topic models, but is often
non-trivial to derive an efficient algorithm for a specific model. It is even
much more challenging when we want to find a fast inference algorithm which
always yields sparse latent representations of documents. In this article, we
introduce a si... | computer science |
1,954 | Learning image representations tied to ego-motion | cs.CV | Understanding how images of objects and scenes behave in response to specific
ego-motions is a crucial aspect of proper visual development, yet existing
visual learning methods are conspicuously disconnected from the physical source
of their images. We propose to exploit proprioceptive motor signals to provide
unsuperv... | computer science |
1,955 | StackGAN: Text to Photo-realistic Image Synthesis with Stacked
Generative Adversarial Networks | cs.CV | Synthesizing high-quality images from text descriptions is a challenging
problem in computer vision and has many practical applications. Samples
generated by existing text-to-image approaches can roughly reflect the meaning
of the given descriptions, but they fail to contain necessary details and vivid
object parts. In... | computer science |
1,956 | EnhanceNet: Single Image Super-Resolution Through Automated Texture
Synthesis | cs.CV | Single image super-resolution is the task of inferring a high-resolution
image from a single low-resolution input. Traditionally, the performance of
algorithms for this task is measured using pixel-wise reconstruction measures
such as peak signal-to-noise ratio (PSNR) which have been shown to correlate
poorly with the ... | computer science |
1,957 | Approximate Bayesian Image Interpretation using Generative Probabilistic
Graphics Programs | cs.AI | The idea of computer vision as the Bayesian inverse problem to computer
graphics has a long history and an appealing elegance, but it has proved
difficult to directly implement. Instead, most vision tasks are approached via
complex bottom-up processing pipelines. Here we show that it is possible to
write short, simple ... | computer science |
1,958 | Inverse Graphics with Probabilistic CAD Models | cs.CV | Recently, multiple formulations of vision problems as probabilistic
inversions of generative models based on computer graphics have been proposed.
However, applications to 3D perception from natural images have focused on
low-dimensional latent scenes, due to challenges in both modeling and
inference. Accounting for th... | computer science |
1,959 | Estimating the intrinsic dimension in fMRI space via dataset fractal
analysis - Counting the `cpu cores' of the human brain | cs.AI | Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool
for localizing and analyzing brain activity. This study focuses on one very
important aspect of the functional properties of human brain, specifically the
estimation of the level of parallelism when performing complex cognitive tasks.
Using fM... | computer science |
1,960 | An Ensemble-based System for Microaneurysm Detection and Diabetic
Retinopathy Grading | cs.CV | Reliable microaneurysm detection in digital fundus images is still an open
issue in medical image processing. We propose an ensemble-based framework to
improve microaneurysm detection. Unlike the well-known approach of considering
the output of multiple classifiers, we propose a combination of internal
components of mi... | computer science |
1,961 | Cross-Domain Visual Matching via Generalized Similarity Measure and
Feature Learning | cs.CV | Cross-domain visual data matching is one of the fundamental problems in many
real-world vision tasks, e.g., matching persons across ID photos and
surveillance videos. Conventional approaches to this problem usually involves
two steps: i) projecting samples from different domains into a common space,
and ii) computing (... | computer science |
1,962 | A Fast Factorization-based Approach to Robust PCA | cs.CV | Robust principal component analysis (RPCA) has been widely used for
recovering low-rank matrices in many data mining and machine learning problems.
It separates a data matrix into a low-rank part and a sparse part. The convex
approach has been well studied in the literature. However, state-of-the-art
algorithms for the... | computer science |
1,963 | X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets | stat.ML | In this paper we propose cross-modal convolutional neural networks (X-CNNs),
a novel biologically inspired type of CNN architectures, treating gradient
descent-specialised CNNs as individual units of processing in a larger-scale
network topology, while allowing for unconstrained information flow and/or
weight sharing b... | computer science |
1,964 | Expert Gate: Lifelong Learning with a Network of Experts | cs.CV | In this paper we introduce a model of lifelong learning, based on a Network
of Experts. New tasks / experts are learned and added to the model
sequentially, building on what was learned before. To ensure scalability of
this process,data from previous tasks cannot be stored and hence is not
available when learning a new... | computer science |
1,965 | Information Pursuit: A Bayesian Framework for Sequential Scene Parsing | cs.CV | Despite enormous progress in object detection and classification, the problem
of incorporating expected contextual relationships among object instances into
modern recognition systems remains a key challenge. In this work we propose
Information Pursuit, a Bayesian framework for scene parsing that combines prior
models ... | computer science |
1,966 | Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with
2D Joint Detections | cs.CV | We propose a method to generate multiple diverse and valid human pose
hypotheses in 3D all consistent with the 2D detection of joints in a monocular
RGB image. We use a novel generative model uniform (unbiased) in the space of
anatomically plausible 3D poses. Our model is compositional (produces a pose by
combining par... | computer science |
1,967 | Robot gains Social Intelligence through Multimodal Deep Reinforcement
Learning | cs.RO | For robots to coexist with humans in a social world like ours, it is crucial
that they possess human-like social interaction skills. Programming a robot to
possess such skills is a challenging task. In this paper, we propose a
Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like
interaction skills thr... | computer science |
1,968 | Show, Attend and Interact: Perceivable Human-Robot Social Interaction
through Neural Attention Q-Network | cs.RO | For a safe, natural and effective human-robot social interaction, it is
essential to develop a system that allows a robot to demonstrate the
perceivable responsive behaviors to complex human behaviors. We introduce the
Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits
human-like social intera... | computer science |
1,969 | Segmentation of skin lesions based on fuzzy classification of pixels and
histogram thresholding | cs.CV | This paper proposes an innovative method for segmentation of skin lesions in
dermoscopy images developed by the authors, based on fuzzy classification of
pixels and histogram thresholding. | computer science |
1,970 | Self corrective Perturbations for Semantic Segmentation and
Classification | cs.CV | Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of re... | computer science |
1,971 | Predicting Cognitive Decline with Deep Learning of Brain Metabolism and
Amyloid Imaging | cs.CV | For effective treatment of Alzheimer disease (AD), it is important to
identify subjects who are most likely to exhibit rapid cognitive decline.
Herein, we developed a novel framework based on a deep convolutional neural
network which can predict future cognitive decline in mild cognitive impairment
(MCI) patients using... | computer science |
1,972 | Static Gesture Recognition using Leap Motion | stat.ML | In this report, an automated bartender system was developed for making orders
in a bar using hand gestures. The gesture recognition of the system was
developed using Machine Learning techniques, where the model was trained to
classify gestures using collected data. The final model used in the system
reached an average ... | computer science |
1,973 | The Conditional Analogy GAN: Swapping Fashion Articles on People Images | stat.ML | We present a novel method to solve image analogy problems : it allows to
learn the relation between paired images present in training data, and then
generalize and generate images that correspond to the relation, but were never
seen in the training set. Therefore, we call the method Conditional Analogy
Generative Adver... | computer science |
1,974 | Distance-based Confidence Score for Neural Network Classifiers | cs.AI | The reliable measurement of confidence in classifiers' predictions is very
important for many applications and is, therefore, an important part of
classifier design. Yet, although deep learning has received tremendous
attention in recent years, not much progress has been made in quantifying the
prediction confidence of... | computer science |
1,975 | StackGAN++: Realistic Image Synthesis with Stacked Generative
Adversarial Networks | cs.CV | Although Generative Adversarial Networks (GANs) have shown remarkable success
in various tasks, they still face challenges in generating high quality images.
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN)
aiming at generating high-resolution photo-realistic images. First, we propose
a two-... | computer science |
1,976 | Memory Aware Synapses: Learning what (not) to forget | cs.CV | Humans can learn in a continuous manner. Old rarely utilized knowledge can be
overwritten by new incoming information while important, frequently used
knowledge is prevented from being erased. In artificial learning systems,
lifelong learning so far has focused mainly on accumulating knowledge over
tasks and overcoming... | computer science |
1,977 | Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care | stat.ML | This paper proposes a real-time embedded fall detection system using a
DVS(Dynamic Vision Sensor) that has never been used for traditional fall
detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
Network). The first contribution is building a DVS Falls Dataset, which made
our network to recogn... | computer science |
1,978 | Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters
in Deep Neural Networks | cs.CV | In an effort to understand the meaning of the intermediate representations
captured by deep networks, recent papers have tried to associate specific
semantic concepts to individual neural network filter responses, where
interesting correlations are often found, largely by focusing on extremal
filter responses. In this ... | computer science |
1,979 | Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning | cs.CV | In recent years, Convolutional Neural Networks (CNNs) have shown remarkable
performance in many computer vision tasks such as object recognition and
detection. However, complex training issues, such as "catastrophic forgetting"
and hyper-parameter tuning, make incremental learning in CNNs a difficult
challenge. In this... | computer science |
1,980 | Generating retinal flow maps from structural optical coherence
tomography with artificial intelligence | cs.CV | Despite significant advances in artificial intelligence (AI) for computer
vision, its application in medical imaging has been limited by the burden and
limits of expert-generated labels. We used images from optical coherence
tomography angiography (OCTA), a relatively new imaging modality that measures
perfusion of the... | computer science |
1,981 | Robust Blind Deconvolution via Mirror Descent | cs.CV | We revisit the Blind Deconvolution problem with a focus on understanding its
robustness and convergence properties. Provable robustness to noise and other
perturbations is receiving recent interest in vision, from obtaining immunity
to adversarial attacks to assessing and describing failure modes of algorithms
in missi... | computer science |
1,982 | Learning to relate images: Mapping units, complex cells and simultaneous
eigenspaces | cs.CV | A fundamental operation in many vision tasks, including motion understanding,
stereopsis, visual odometry, or invariant recognition, is establishing
correspondences between images or between images and data from other
modalities. We present an analysis of the role that multiplicative interactions
play in learning such ... | computer science |
1,983 | Playing Doom with SLAM-Augmented Deep Reinforcement Learning | cs.AI | A number of recent approaches to policy learning in 2D game domains have been
successful going directly from raw input images to actions. However when
employed in complex 3D environments, they typically suffer from challenges
related to partial observability, combinatorial exploration spaces, path
planning, and a scarc... | computer science |
1,984 | Sparse Factorization Layers for Neural Networks with Limited Supervision | cs.CV | Whereas CNNs have demonstrated immense progress in many vision problems, they
suffer from a dependence on monumental amounts of labeled training data. On the
other hand, dictionary learning does not scale to the size of problems that
CNNs can handle, despite being very effective at low-level vision tasks such as
denois... | computer science |
1,985 | Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA
for the Design of Training Graphs | cs.AI | Slow feature analysis (SFA) is an unsupervised learning algorithm that
extracts slowly varying features from a time series. Graph-based SFA (GSFA) is
a supervised extension that can solve regression problems if followed by a
post-processing regression algorithm. A training graph specifies arbitrary
connections between ... | computer science |
1,986 | Human Pose Estimation in Space and Time using 3D CNN | cs.CV | This paper explores the capabilities of convolutional neural networks to deal
with a task that is easily manageable for humans: perceiving 3D pose of a human
body from varying angles. However, in our approach, we are restricted to using
a monocular vision system. For this purpose, we apply a convolutional neural
networ... | computer science |
1,987 | Encoder Based Lifelong Learning | cs.CV | This paper introduces a new lifelong learning solution where a single model
is trained for a sequence of tasks. The main challenge that vision systems face
in this context is catastrophic forgetting: as they tend to adapt to the most
recently seen task, they lose performance on the tasks that were learned
previously. O... | computer science |
1,988 | AirDraw: Leveraging Smart Watch Motion Sensors for Mobile Human Computer
Interactions | cs.CV | Wearable computing is one of the fastest growing technologies today. Smart
watches are poised to take over at least of half the wearable devices market in
the near future. Smart watch screen size, however, is a limiting factor for
growth, as it restricts practical text input. On the other hand, wearable
devices have so... | computer science |
1,989 | Morphological Error Detection in 3D Segmentations | cs.CV | Deep learning algorithms for connectomics rely upon localized classification,
rather than overall morphology. This leads to a high incidence of erroneously
merged objects. Humans, by contrast, can easily detect such errors by acquiring
intuition for the correct morphology of objects. Biological neurons have
complicated... | computer science |
1,990 | End-to-end Training for Whole Image Breast Cancer Diagnosis using An All
Convolutional Design | cs.CV | We develop an end-to-end training algorithm for whole-image breast cancer
diagnosis based on mammograms. It requires lesion annotations only at the first
stage of training. After that, a whole image classifier can be trained using
only image level labels. This greatly reduced the reliance on lesion
annotations. Our app... | computer science |
1,991 | Explaining Aviation Safety Incidents Using Deep Temporal Multiple
Instance Learning | cs.CV | Although aviation accidents are rare, safety incidents occur more frequently
and require a careful analysis to detect and mitigate risks in a timely manner.
Analyzing safety incidents using operational data and producing event-based
explanations is invaluable to airline companies as well as to governing
organizations s... | computer science |
1,992 | Using KL-divergence to focus Deep Visual Explanation | cs.AI | We present a method for explaining the image classification predictions of
deep convolution neural networks, by highlighting the pixels in the image which
influence the final class prediction. Our method requires the identification of
a heuristic method to select parameters hypothesized to be most relevant in
this pred... | computer science |
1,993 | Pose-Normalized Image Generation for Person Re-identification | cs.CV | Person Re-identification (re-id) faces two major challenges: the lack of
cross-view paired training data and learning discriminative identity-sensitive
and view-invariant features in the presence of large pose variations. In this
work, we address both problems by proposing a novel deep person image
generation model for... | computer science |
1,994 | Frame-Recurrent Video Super-Resolution | cs.CV | Recent advances in video super-resolution have shown that convolutional
neural networks combined with motion compensation are able to merge information
from multiple low-resolution (LR) frames to generate high-quality images.
Current state-of-the-art methods process a batch of LR frames to generate a
single high-resolu... | computer science |
1,995 | A Method for Restoring the Training Set Distribution in an Image
Classifier | stat.ML | Convolutional Neural Networks are a well-known staple of modern image
classification. However, it can be difficult to assess the quality and
robustness of such models. Deep models are known to perform well on a given
training and estimation set, but can easily be fooled by data that is
specifically generated for the pu... | computer science |
1,996 | Discrete and fuzzy dynamical genetic programming in the XCSF learning
classifier system | cs.AI | A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular,... | computer science |
1,997 | Protein Secondary Structure Prediction Using Cascaded Convolutional and
Recurrent Neural Networks | cs.AI | Protein secondary structure prediction is an important problem in
bioinformatics. Inspired by the recent successes of deep neural networks, in
this paper, we propose an end-to-end deep network that predicts protein
secondary structures from integrated local and global contextual features. Our
deep architecture leverage... | computer science |
1,998 | Reinforcement Learning Using Quantum Boltzmann Machines | cs.AI | We investigate whether quantum annealers with select chip layouts can
outperform classical computers in reinforcement learning tasks. We associate a
transverse field Ising spin Hamiltonian with a layout of qubits similar to that
of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to
numerically ... | computer science |
1,999 | The Fundamental Learning Problem that Genetic Algorithms with Uniform
Crossover Solve Efficiently and Repeatedly As Evolution Proceeds | cs.NE | This paper establishes theoretical bonafides for implicit concurrent
multivariate effect evaluation--implicit concurrency for short---a broad and
versatile computational learning efficiency thought to underlie
general-purpose, non-local, noise-tolerant optimization in genetic algorithms
with uniform crossover (UGAs). W... | computer science |
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