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2,900 | FAME: Face Association through Model Evolution | cs.CV | We attack the problem of learning face models for public faces from
weakly-labelled images collected from web through querying a name. The data is
very noisy even after face detection, with several irrelevant faces
corresponding to other people. We propose a novel method, Face Association
through Model Evolution (FAME)... | computer science |
2,901 | ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image
Collections | cs.CV | Discovering visual knowledge from weakly labeled data is crucial to scale up
computer vision recognition system, since it is expensive to obtain fully
labeled data for a large number of concept categories. In this paper, we
propose ConceptLearner, which is a scalable approach to discover visual
concepts from weakly lab... | computer science |
2,902 | Double-Base Asymmetric AdaBoost | cs.CV | Based on the use of different exponential bases to define class-dependent
error bounds, a new and highly efficient asymmetric boosting scheme, coined as
AdaBoostDB (Double-Base), is proposed. Supported by a fully theoretical
derivation procedure, unlike most of the other approaches in the literature,
our algorithm pres... | computer science |
2,903 | Action-Conditional Video Prediction using Deep Networks in Atari Games | cs.LG | Motivated by vision-based reinforcement learning (RL) problems, in particular
Atari games from the recent benchmark Aracade Learning Environment (ALE), we
consider spatio-temporal prediction problems where future (image-)frames are
dependent on control variables or actions as well as previous frames. While not
composed... | computer science |
2,904 | Understanding symmetries in deep networks | cs.LG | Recent works have highlighted scale invariance or symmetry present in the
weight space of a typical deep network and the adverse effect it has on the
Euclidean gradient based stochastic gradient descent optimization. In this
work, we show that a commonly used deep network, which uses convolution, batch
normalization, r... | computer science |
2,905 | Symmetry-invariant optimization in deep networks | cs.LG | Recent works have highlighted scale invariance or symmetry that is present in
the weight space of a typical deep network and the adverse effect that it has
on the Euclidean gradient based stochastic gradient descent optimization. In
this work, we show that these and other commonly used deep networks, such as
those whic... | computer science |
2,906 | Visual Language Modeling on CNN Image Representations | cs.CV | Measuring the naturalness of images is important to generate realistic images
or to detect unnatural regions in images. Additionally, a method to measure
naturalness can be complementary to Convolutional Neural Network (CNN) based
features, which are known to be insensitive to the naturalness of images.
However, most p... | computer science |
2,907 | Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain
Decoding | cs.LG | We represent the sequence of fMRI (Functional Magnetic Resonance Imaging)
brain volumes recorded during a cognitive stimulus by a graph which consists of
a set of local meshes. The corresponding cognitive process, encoded in the
brain, is then represented by these meshes each of which is estimated assuming
a linear rel... | computer science |
2,908 | Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning
and Large-Scale Data Collection | cs.LG | We describe a learning-based approach to hand-eye coordination for robotic
grasping from monocular images. To learn hand-eye coordination for grasping, we
trained a large convolutional neural network to predict the probability that
task-space motion of the gripper will result in successful grasps, using only
monocular ... | computer science |
2,909 | Learning Domain-Invariant Subspace using Domain Features and
Independence Maximization | cs.CV | Domain adaptation algorithms are useful when the distributions of the
training and the test data are different. In this paper, we focus on the
problem of instrumental variation and time-varying drift in the field of
sensors and measurement, which can be viewed as discrete and continuous
distributional change in the fea... | computer science |
2,910 | Control of Memory, Active Perception, and Action in Minecraft | cs.AI | In this paper, we introduce a new set of reinforcement learning (RL) tasks in
Minecraft (a flexible 3D world). We then use these tasks to systematically
compare and contrast existing deep reinforcement learning (DRL) architectures
with our new memory-based DRL architectures. These tasks are designed to
emphasize, in a ... | computer science |
2,911 | Generating Images Part by Part with Composite Generative Adversarial
Networks | cs.AI | Image generation remains a fundamental problem in artificial intelligence in
general and deep learning in specific. The generative adversarial network (GAN)
was successful in generating high quality samples of natural images. We propose
a model called composite generative adversarial network, that reveals the
complex s... | computer science |
2,912 | Autonomous Grounding of Visual Field Experience through Sensorimotor
Prediction | cs.RO | In a developmental framework, autonomous robots need to explore the world and
learn how to interact with it. Without an a priori model of the system, this
opens the challenging problem of having robots master their interface with the
world: how to perceive their environment using their sensors, and how to act in
it usi... | computer science |
2,913 | Deeply Semantic Inductive Spatio-Temporal Learning | cs.AI | We present an inductive spatio-temporal learning framework rooted in
inductive logic programming. With an emphasis on visuo-spatial language, logic,
and cognition, the framework supports learning with relational spatio-temporal
features identifiable in a range of domains involving the processing and
interpretation of d... | computer science |
2,914 | Deep Markov Random Field for Image Modeling | cs.CV | Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitation... | computer science |
2,915 | Contextual RNN-GANs for Abstract Reasoning Diagram Generation | cs.CV | Understanding, predicting, and generating object motions and transformations
is a core problem in artificial intelligence. Modeling sequences of evolving
images may provide better representations and models of motion and may
ultimately be used for forecasting, simulation, or video generation.
Diagrammatic Abstract Reas... | computer science |
2,916 | Deep Visual Foresight for Planning Robot Motion | cs.LG | A key challenge in scaling up robot learning to many skills and environments
is removing the need for human supervision, so that robots can collect their
own data and improve their own performance without being limited by the cost of
requesting human feedback. Model-based reinforcement learning holds the promise
of ena... | computer science |
2,917 | Domain Adaptation with Soft-margin multiple feature-kernel learning
beats Deep Learning for surveillance face recognition | cs.CV | Face recognition (FR) is the most preferred mode for biometric-based
surveillance, due to its passive nature of detecting subjects, amongst all
different types of biometric traits. FR under surveillance scenario does not
give satisfactory performance due to low contrast, noise and poor illumination
conditions on probes... | computer science |
2,918 | Feature base fusion for splicing forgery detection based on neuro fuzzy | cs.CV | Most of researches on image forensics have been mainly focused on detection
of artifacts introduced by a single processing tool. They lead in the
development of many specialized algorithms looking for one or more particular
footprints under specific settings. Naturally, the performance of such
algorithms are not perfec... | computer science |
2,919 | Ensembles of Deep LSTM Learners for Activity Recognition using Wearables | cs.LG | Recently, deep learning (DL) methods have been introduced very successfully
into human activity recognition (HAR) scenarios in ubiquitous and wearable
computing. Especially the prospect of overcoming the need for manual feature
design combined with superior classification capabilities render deep neural
networks very a... | computer science |
2,920 | Bootstrapping Labelled Dataset Construction for Cow Tracking and
Behavior Analysis | cs.CV | This paper introduces a new approach to the long-term tracking of an object
in a challenging environment. The object is a cow and the environment is an
enclosure in a cowshed. Some of the key challenges in this domain are a
cluttered background, low contrast and high similarity between moving objects
which greatly redu... | computer science |
2,921 | Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR)
Approach to Understanding Deep Neural Networks | cs.CV | In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an
approach to visualize and understand the decisions made by deep neural networks
(DNNs) given a specific input. CLEAR facilitates the visualization of attentive
regions and levels of interest of DNNs during the decision-making process. It
also enable... | computer science |
2,922 | Fast k-means based on KNN Graph | cs.LG | In the era of big data, k-means clustering has been widely adopted as a basic
processing tool in various contexts. However, its computational cost could be
prohibitively high as the data size and the cluster number are large. It is
well known that the processing bottleneck of k-means lies in the operation of
seeking cl... | computer science |
2,923 | Multimodal Affect Analysis for Product Feedback Assessment | cs.HC | Consumers often react expressively to products such as food samples, perfume,
jewelry, sunglasses, and clothing accessories. This research discusses a
multimodal affect recognition system developed to classify whether a consumer
likes or dislikes a product tested at a counter or kiosk, by analyzing the
consumer's facia... | computer science |
2,924 | Learning Spatiotemporal Features for Infrared Action Recognition with 3D
Convolutional Neural Networks | cs.CV | Infrared (IR) imaging has the potential to enable more robust action
recognition systems compared to visible spectrum cameras due to lower
sensitivity to lighting conditions and appearance variability. While the action
recognition task on videos collected from visible spectrum imaging has received
much attention, actio... | computer science |
2,925 | Continual Learning with Deep Generative Replay | cs.AI | Attempts to train a comprehensive artificial intelligence capable of solving
multiple tasks have been impeded by a chronic problem called catastrophic
forgetting. Although simply replaying all previous data alleviates the problem,
it requires large memory and even worse, often infeasible in real world
applications wher... | computer science |
2,926 | Conditional generation of multi-modal data using constrained embedding
space mapping | cs.LG | We present a conditional generative model that maps low-dimensional
embeddings of multiple modalities of data to a common latent space hence
extracting semantic relationships between them. The embedding specific to a
modality is first extracted and subsequently a constrained optimization
procedure is performed to proje... | computer science |
2,927 | RegNet: Multimodal Sensor Registration Using Deep Neural Networks | cs.CV | In this paper, we present RegNet, the first deep convolutional neural network
(CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between
multimodal sensors, exemplified using a scanning LiDAR and a monocular camera.
Compared to existing approaches, RegNet casts all three conventional
calibration steps (f... | computer science |
2,928 | Stable Distribution Alignment Using the Dual of the Adversarial Distance | cs.LG | Methods that align distributions by minimizing an adversarial distance
between them have recently achieved impressive results. However, these
approaches are difficult to optimize with gradient descent and they often do
not converge well without careful hyperparameter tuning and proper
initialization. We investigate whe... | computer science |
2,929 | Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in
Changing Environments | cs.RO | Object detection is an essential task for autonomous robots operating in
dynamic and changing environments. A robot should be able to detect objects in
the presence of sensor noise that can be induced by changing lighting
conditions for cameras and false depth readings for range sensors, especially
RGB-D cameras. To ta... | computer science |
2,930 | Measuring Catastrophic Forgetting in Neural Networks | cs.AI | Deep neural networks are used in many state-of-the-art systems for machine
perception. Once a network is trained to do a specific task, e.g., bird
classification, it cannot easily be trained to do new tasks, e.g.,
incrementally learning to recognize additional bird species or learning an
entirely different task such as... | computer science |
2,931 | DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation
of Self-Reported Pain | cs.CV | Previous research on automatic pain estimation from facial expressions has
focused primarily on "one-size-fits-all" metrics (such as PSPI). In this work,
we focus on directly estimating each individual's self-reported visual-analog
scale (VAS) pain metric, as this is considered the gold standard for pain
measurement. T... | computer science |
2,932 | Learning 6-DOF Grasping Interaction with Deep Geometry-aware 3D
Representations | cs.RO | This paper focuses on the problem of learning 6-DOF grasping with a parallel
jaw gripper in simulation. We propose the notion of a geometry-aware
representation in grasping based on the assumption that knowledge of 3D
geometry is at the heart of interaction. Our key idea is constraining and
regularizing grasping intera... | computer science |
2,933 | Subspace Selection to Suppress Confounding Source Domain Information in
AAM Transfer Learning | cs.CV | Active appearance models (AAMs) are a class of generative models that have
seen tremendous success in face analysis. However, model learning depends on
the availability of detailed annotation of canonical landmark points. As a
result, when accurate AAM fitting is required on a different set of variations
(expression, p... | computer science |
2,934 | Learning Loss for Knowledge Distillation with Conditional Adversarial
Networks | cs.LG | There is an increasing interest on accelerating neural networks for real-time
applications. We study the student-teacher strategy, in which a small and fast
student network is trained with the auxiliary information provided by a large
and accurate teacher network. We use conditional adversarial networks to learn
the lo... | computer science |
2,935 | End-to-End United Video Dehazing and Detection | cs.CV | The recent development of CNN-based image dehazing has revealed the
effectiveness of end-to-end modeling. However, extending the idea to end-to-end
video dehazing has not been explored yet. In this paper, we propose an
End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal
consistency between consecutive ... | computer science |
2,936 | Multi-Label Zero-Shot Human Action Recognition via Joint Latent
Embedding | cs.CV | Human action recognition refers to automatic recognizing human actions from a
video clip, which is one of the most challenging tasks in computer vision. In
reality, a video stream is often weakly-annotated with a set of relevant human
action labels at a global level rather than assigning each label to a specific
video ... | computer science |
2,937 | IQ of Neural Networks | cs.LG | IQ tests are an accepted method for assessing human intelligence. The tests
consist of several parts that must be solved under a time constraint. Of all
the tested abilities, pattern recognition has been found to have the highest
correlation with general intelligence. This is primarily because pattern
recognition is th... | computer science |
2,938 | Self-Supervised Visual Planning with Temporal Skip Connections | cs.RO | In order to autonomously learn wide repertoires of complex skills, robots
must be able to learn from their own autonomously collected data, without human
supervision. One learning signal that is always available for autonomously
collected data is prediction: if a robot can learn to predict the future, it
can use this p... | computer science |
2,939 | Gradient-free Policy Architecture Search and Adaptation | cs.LG | We develop a method for policy architecture search and adaptation via
gradient-free optimization which can learn to perform autonomous driving tasks.
By learning from both demonstration and environmental reward we develop a model
that can learn with relatively few early catastrophic failures. We first learn
an architec... | computer science |
2,940 | Spontaneous Symmetry Breaking in Neural Networks | stat.CO | We propose a framework to understand the unprecedented performance and
robustness of deep neural networks using field theory. Correlations between the
weights within the same layer can be described by symmetries in that layer, and
networks generalize better if such symmetries are broken to reduce the
redundancies of th... | computer science |
2,941 | Separation of Water and Fat Magnetic Resonance Imaging Signals Using
Deep Learning with Convolutional Neural Networks | cs.CV | Purpose: A new method for magnetic resonance (MR) imaging water-fat
separation using a convolutional neural network (ConvNet) and deep learning
(DL) is presented. Feasibility of the method with complex and magnitude images
is demonstrated with a series of patient studies and accuracy of predicted
quantitative values is... | computer science |
2,942 | Recurrent Autoregressive Networks for Online Multi-Object Tracking | cs.CV | The main challenge of online multi-object tracking is to reliably associate
object trajectories with detections in each video frame based on their tracking
history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
temporal generative modeling framework to characterize the appearance and
motion dyn... | computer science |
2,943 | Self-Supervised Intrinsic Image Decomposition | cs.CV | Intrinsic decomposition from a single image is a highly challenging task, due
to its inherent ambiguity and the scarcity of training data. In contrast to
traditional fully supervised learning approaches, in this paper we propose
learning intrinsic image decomposition by explaining the input image. Our
model, the Render... | computer science |
2,944 | Driven to Distraction: Self-Supervised Distractor Learning for Robust
Monocular Visual Odometry in Urban Environments | cs.RO | We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which ... | computer science |
2,945 | MAVOT: Memory-Augmented Video Object Tracking | cs.CV | We introduce a one-shot learning approach for video object tracking. The
proposed algorithm requires seeing the object to be tracked only once, and
employs an external memory to store and remember the evolving features of the
foreground object as well as backgrounds over time during tracking. With the
relevant memory r... | computer science |
2,946 | Learning to cluster in order to transfer across domains and tasks | cs.LG | This paper introduces a novel method to perform transfer learning across
domains and tasks, formulating it as a problem of learning to cluster. The key
insight is that, in addition to features, we can transfer similarity
information and this is sufficient to learn a similarity function and
clustering network to perform... | computer science |
2,947 | NAG: Network for Adversary Generation | cs.CV | Adversarial perturbations can pose a serious threat for deploying machine
learning systems. Recent works have shown existence of image-agnostic
perturbations that can fool classifiers over most natural images. Existing
methods present optimization approaches that solve for a fooling objective with
an imperceptibility c... | computer science |
2,948 | AI2-THOR: An Interactive 3D Environment for Visual AI | cs.CV | We introduce The House Of inteRactions (THOR), a framework for visual AI
research, available at http://ai2thor.allenai.org. AI2-THOR consists of near
photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes
and interact with objects to perform tasks. AI2-THOR enables research in many
different domai... | computer science |
2,949 | Generalizable Data-free Objective for Crafting Universal Adversarial
Perturbations | cs.CV | Machine learning models are susceptible to adversarial perturbations: small
changes to input that can cause large changes in output. It is also
demonstrated that there exist input-agnostic perturbations, called universal
adversarial perturbations, which can change the inference of target model on
most of the data sampl... | computer science |
2,950 | FastNet | cs.CV | Inception and the Resnet family of Convolutional Neural Network
archi-tectures have broken records in the past few years, but recent state of
the art models have also incurred very high computational cost in terms of
training, inference and model size. Making the deployment of these models on
Edge devices, impractical.... | computer science |
2,951 | Fooling OCR Systems with Adversarial Text Images | cs.LG | We demonstrate that state-of-the-art optical character recognition (OCR)
based on deep learning is vulnerable to adversarial images. Minor modifications
to images of printed text, which do not change the meaning of the text to a
human reader, cause the OCR system to "recognize" a different text where
certain words chos... | computer science |
2,952 | A dataset and architecture for visual reasoning with a working memory | cs.AI | A vexing problem in artificial intelligence is reasoning about events that
occur in complex, changing visual stimuli such as in video analysis or game
play. Inspired by a rich tradition of visual reasoning and memory in cognitive
psychology and neuroscience, we developed an artificial, configurable visual
question and ... | computer science |
2,953 | Towards Universal Representation for Unseen Action Recognition | cs.CV | Unseen Action Recognition (UAR) aims to recognise novel action categories
without training examples. While previous methods focus on inner-dataset
seen/unseen splits, this paper proposes a pipeline using a large-scale training
source to achieve a Universal Representation (UR) that can generalise to a more
realistic Cro... | computer science |
2,954 | Pattern Recognition for Conditionally Independent Data | cs.LG | In this work we consider the task of relaxing the i.i.d assumption in pattern
recognition (or classification), aiming to make existing learning algorithms
applicable to a wider range of tasks. Pattern recognition is guessing a
discrete label of some object based on a set of given examples (pairs of
objects and labels).... | computer science |
2,955 | Regularity of Position Sequences | cs.CV | A person is given a numbered sequence of positions on a sheet of paper. The
person is asked, "Which will be the next (or the next after that) position?"
Everyone has an opinion as to how he or she would proceed. There are regular
sequences for which there is general agreement on how to continue. However,
there are less... | computer science |
2,956 | Learning Hierarchical Sparse Representations using Iterative Dictionary
Learning and Dimension Reduction | cs.LG | This paper introduces an elemental building block which combines Dictionary
Learning and Dimension Reduction (DRDL). We show how this foundational element
can be used to iteratively construct a Hierarchical Sparse Representation (HSR)
of a sensory stream. We compare our approach to existing models showing the
generalit... | computer science |
2,957 | Nearest Prime Simplicial Complex for Object Recognition | cs.LG | The structure representation of data distribution plays an important role in
understanding the underlying mechanism of generating data. In this paper, we
propose nearest prime simplicial complex approaches (NSC) by utilizing
persistent homology to capture such structures. Assuming that each class is
represented with a ... | computer science |
2,958 | A New Clustering Algorithm Based Upon Flocking On Complex Network | cs.LG | We have proposed a model based upon flocking on a complex network, and then
developed two clustering algorithms on the basis of it. In the algorithms,
firstly a \textit{k}-nearest neighbor (knn) graph as a weighted and directed
graph is produced among all data points in a dataset each of which is regarded
as an agent w... | computer science |
2,959 | Tracking using explanation-based modeling | cs.LG | We study the tracking problem, namely, estimating the hidden state of an
object over time, from unreliable and noisy measurements. The standard
framework for the tracking problem is the generative framework, which is the
basis of solutions such as the Bayesian algorithm and its approximation, the
particle filters. Howe... | computer science |
2,960 | An Empirical Evaluation of Four Algorithms for Multi-Class
Classification: Mart, ABC-Mart, Robust LogitBoost, and ABC-LogitBoost | cs.LG | This empirical study is mainly devoted to comparing four tree-based boosting
algorithms: mart, abc-mart, robust logitboost, and abc-logitboost, for
multi-class classification on a variety of publicly available datasets. Some of
those datasets have been thoroughly tested in prior studies using a broad range
of classific... | computer science |
2,961 | Using Feature Weights to Improve Performance of Neural Networks | cs.LG | Different features have different relevance to a particular learning problem.
Some features are less relevant; while some very important. Instead of
selecting the most relevant features using feature selection, an algorithm can
be given this knowledge of feature importance based on expert opinion or prior
learning. Lea... | computer science |
2,962 | Higher-Order Markov Tag-Topic Models for Tagged Documents and Images | cs.CV | This paper studies the topic modeling problem of tagged documents and images.
Higher-order relations among tagged documents and images are major and
ubiquitous characteristics, and play positive roles in extracting reliable and
interpretable topics. In this paper, we propose the tag-topic models (TTM) to
depict such hi... | computer science |
2,963 | Unsupervised Discovery of Mid-Level Discriminative Patches | cs.CV | The goal of this paper is to discover a set of discriminative patches which
can serve as a fully unsupervised mid-level visual representation. The desired
patches need to satisfy two requirements: 1) to be representative, they need to
occur frequently enough in the visual world; 2) to be discriminative, they need
to be... | computer science |
2,964 | A new look at reweighted message passing | cs.AI | We propose a new family of message passing techniques for MAP estimation in
graphical models which we call {\em Sequential Reweighted Message Passing}
(SRMP). Special cases include well-known techniques such as {\em Min-Sum
Diffusion} (MSD) and a faster {\em Sequential Tree-Reweighted Message Passing}
(TRW-S). Importan... | computer science |
2,965 | Deep Learning for Medical Image Segmentation | cs.LG | This report provides an overview of the current state of the art deep
learning architectures and optimisation techniques, and uses the ADNI
hippocampus MRI dataset as an example to compare the effectiveness and
efficiency of different convolutional architectures on the task of patch-based
3-dimensional hippocampal segm... | computer science |
2,966 | Learning to Track at 100 FPS with Deep Regression Networks | cs.CV | Machine learning techniques are often used in computer vision due to their
ability to leverage large amounts of training data to improve performance.
Unfortunately, most generic object trackers are still trained from scratch
online and do not benefit from the large number of videos that are readily
available for offlin... | computer science |
2,967 | How important are Deformable Parts in the Deformable Parts Model? | cs.CV | The main stated contribution of the Deformable Parts Model (DPM) detector of
Felzenszwalb et al. (over the Histogram-of-Oriented-Gradients approach of Dalal
and Triggs) is the use of deformable parts. A secondary contribution is the
latent discriminative learning. Tertiary is the use of multiple components. A
common be... | computer science |
2,968 | Modeling Latent Variable Uncertainty for Loss-based Learning | cs.LG | We consider the problem of parameter estimation using weakly supervised
datasets, where a training sample consists of the input and a partially
specified annotation, which we refer to as the output. The missing information
in the annotation is modeled using latent variables. Previous methods
overburden a single distrib... | computer science |
2,969 | Discriminative Functional Connectivity Measures for Brain Decoding | cs.AI | We propose a statistical learning model for classifying cognitive processes
based on distributed patterns of neural activation in the brain, acquired via
functional magnetic resonance imaging (fMRI). In the proposed learning method,
local meshes are formed around each voxel. The distance between voxels in the
mesh is d... | computer science |
2,970 | Probabilistic Zero-shot Classification with Semantic Rankings | cs.LG | In this paper we propose a non-metric ranking-based representation of
semantic similarity that allows natural aggregation of semantic information
from multiple heterogeneous sources. We apply the ranking-based representation
to zero-shot learning problems, and present deterministic and probabilistic
zero-shot classifie... | computer science |
2,971 | Diverse Landmark Sampling from Determinantal Point Processes for
Scalable Manifold Learning | cs.LG | High computational costs of manifold learning prohibit its application for
large point sets. A common strategy to overcome this problem is to perform
dimensionality reduction on selected landmarks and to successively embed the
entire dataset with the Nystr\"om method. The two main challenges that arise
are: (i) the lan... | computer science |
2,972 | Self-critical Sequence Training for Image Captioning | cs.LG | Recently it has been shown that policy-gradient methods for reinforcement
learning can be utilized to train deep end-to-end systems directly on
non-differentiable metrics for the task at hand. In this paper we consider the
problem of optimizing image captioning systems using reinforcement learning,
and show that by car... | computer science |
2,973 | Action-Driven Object Detection with Top-Down Visual Attentions | cs.CV | A dominant paradigm for deep learning based object detection relies on a
"bottom-up" approach using "passive" scoring of class agnostic proposals. These
approaches are efficient but lack of holistic analysis of scene-level context.
In this paper, we present an "action-driven" detection mechanism using our
"top-down" vi... | computer science |
2,974 | Meta-Unsupervised-Learning: A supervised approach to unsupervised
learning | cs.LG | We introduce a new paradigm to investigate unsupervised learning, reducing
unsupervised learning to supervised learning. Specifically, we mitigate the
subjectivity in unsupervised decision-making by leveraging knowledge acquired
from prior, possibly heterogeneous, supervised learning tasks. We demonstrate
the versatili... | computer science |
2,975 | Image Classification Using SVMs: One-against-One Vs One-against-All | cs.LG | Support Vector Machines (SVMs) are a relatively new supervised classification
technique to the land cover mapping community. They have their roots in
Statistical Learning Theory and have gained prominence because they are robust,
accurate and are effective even when using a small training sample. By their
nature SVMs a... | computer science |
2,976 | What you need to know about the state-of-the-art computational models of
object-vision: A tour through the models | cs.CV | Models of object vision have been of great interest in computer vision and
visual neuroscience. During the last decades, several models have been
developed to extract visual features from images for object recognition tasks.
Some of these were inspired by the hierarchical structure of primate visual
system, and some ot... | computer science |
2,977 | Consensus Message Passing for Layered Graphical Models | cs.CV | Generative models provide a powerful framework for probabilistic reasoning.
However, in many domains their use has been hampered by the practical
difficulties of inference. This is particularly the case in computer vision,
where models of the imaging process tend to be large, loopy and layered. For
this reason bottom-u... | computer science |
2,978 | Visual Learning of Arithmetic Operations | cs.LG | A simple Neural Network model is presented for end-to-end visual learning of
arithmetic operations from pictures of numbers. The input consists of two
pictures, each showing a 7-digit number. The output, also a picture, displays
the number showing the result of an arithmetic operation (e.g., addition or
subtraction) on... | computer science |
2,979 | Shedding Light on the Asymmetric Learning Capability of AdaBoost | cs.LG | In this paper, we propose a different insight to analyze AdaBoost. This
analysis reveals that, beyond some preconceptions, AdaBoost can be directly
used as an asymmetric learning algorithm, preserving all its theoretical
properties. A novel class-conditional description of AdaBoost, which models the
actual asymmetric b... | computer science |
2,980 | Untangling AdaBoost-based Cost-Sensitive Classification. Part I:
Theoretical Perspective | cs.CV | Boosting algorithms have been widely used to tackle a plethora of problems.
In the last few years, a lot of approaches have been proposed to provide
standard AdaBoost with cost-sensitive capabilities, each with a different
focus. However, for the researcher, these algorithms shape a tangled set with
diffuse differences... | computer science |
2,981 | Untangling AdaBoost-based Cost-Sensitive Classification. Part II:
Empirical Analysis | cs.CV | A lot of approaches, each following a different strategy, have been proposed
in the literature to provide AdaBoost with cost-sensitive properties. In the
first part of this series of two papers, we have presented these algorithms in
a homogeneous notational framework, proposed a clustering scheme for them and
performed... | computer science |
2,982 | Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds,
Language and Trajectories | cs.RO | A robot operating in a real-world environment needs to perform reasoning over
a variety of sensor modalities such as vision, language and motion
trajectories. However, it is extremely challenging to manually design features
relating such disparate modalities. In this work, we introduce an algorithm
that learns to embed... | computer science |
2,983 | Spectral-Spatial Classification of Hyperspectral Image Using
Autoencoders | cs.CV | Hyperspectral image (HSI) classification is a hot topic in the remote sensing
community. This paper proposes a new framework of spectral-spatial feature
extraction for HSI classification, in which for the first time the concept of
deep learning is introduced. Specifically, the model of autoencoder is
exploited in our f... | computer science |
2,984 | Convolutional Models for Joint Object Categorization and Pose Estimation | cs.CV | In the task of Object Recognition, there exists a dichotomy between the
categorization of objects and estimating object pose, where the former
necessitates a view-invariant representation, while the latter requires a
representation capable of capturing pose information over different categories
of objects. With the ris... | computer science |
2,985 | A New Smooth Approximation to the Zero One Loss with a Probabilistic
Interpretation | cs.CV | We examine a new form of smooth approximation to the zero one loss in which
learning is performed using a reformulation of the widely used logistic
function. Our approach is based on using the posterior mean of a novel
generalized Beta-Bernoulli formulation. This leads to a generalized logistic
function that approximat... | computer science |
2,986 | Unsupervised Learning of Visual Structure using Predictive Generative
Networks | cs.LG | The ability to predict future states of the environment is a central pillar
of intelligence. At its core, effective prediction requires an internal model
of the world and an understanding of the rules by which the world changes.
Here, we explore the internal models developed by deep neural networks trained
using a loss... | computer science |
2,987 | Hand Pose Estimation through Semi-Supervised and Weakly-Supervised
Learning | cs.CV | We propose a method for hand pose estimation based on a deep regressor
trained on two different kinds of input. Raw depth data is fused with an
intermediate representation in the form of a segmentation of the hand into
parts. This intermediate representation contains important topological
information and provides usefu... | computer science |
2,988 | Attribute2Image: Conditional Image Generation from Visual Attributes | cs.LG | This paper investigates a novel problem of generating images from visual
attributes. We model the image as a composite of foreground and background and
develop a layered generative model with disentangled latent variables that can
be learned end-to-end using a variational auto-encoder. We experiment with
natural images... | computer science |
2,989 | Weakly-supervised Disentangling with Recurrent Transformations for 3D
View Synthesis | cs.LG | An important problem for both graphics and vision is to synthesize novel
views of a 3D object from a single image. This is particularly challenging due
to the partial observability inherent in projecting a 3D object onto the image
space, and the ill-posedness of inferring object shape and pose. However, we
can train a ... | computer science |
2,990 | $\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of
JPEG-Compressed Images | cs.CV | In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast
restoration model to remove artifacts of JPEG compressed images. It leverages
the large learning capacity of deep networks, as well as the problem-specific
expertise that was hardly incorporated in the past design of deep
architectures. For the lat... | computer science |
2,991 | Studying Very Low Resolution Recognition Using Deep Networks | cs.CV | Visual recognition research often assumes a sufficient resolution of the
region of interest (ROI). That is usually violated in practice, inspiring us to
explore the Very Low Resolution Recognition (VLRR) problem. Typically, the ROI
in a VLRR problem can be smaller than $16 \times 16$ pixels, and is challenging
to be re... | computer science |
2,992 | Recognition of Visually Perceived Compositional Human Actions by
Multiple Spatio-Temporal Scales Recurrent Neural Networks | cs.CV | The current paper proposes a novel neural network model for recognizing
visually perceived human actions. The proposed multiple spatio-temporal scales
recurrent neural network (MSTRNN) model is derived by introducing multiple
timescale recurrent dynamics to the conventional convolutional neural network
model. One of th... | computer science |
2,993 | Suppressing the Unusual: towards Robust CNNs using Symmetric Activation
Functions | cs.CV | Many deep Convolutional Neural Networks (CNN) make incorrect predictions on
adversarial samples obtained by imperceptible perturbations of clean samples.
We hypothesize that this is caused by a failure to suppress unusual signals
within network layers. As remedy we propose the use of Symmetric Activation
Functions (SAF... | computer science |
2,994 | Action-Affect Classification and Morphing using Multi-Task
Representation Learning | cs.CV | Most recent work focused on affect from facial expressions, and not as much
on body. This work focuses on body affect analysis. Affect does not occur in
isolation. Humans usually couple affect with an action in natural interactions;
for example, a person could be talking and smiling. Recognizing body affect in
sequence... | computer science |
2,995 | Conditional Similarity Networks | cs.CV | What makes images similar? To measure the similarity between images, they are
typically embedded in a feature-vector space, in which their distance preserve
the relative dissimilarity. However, when learning such similarity embeddings
the simplifying assumption is commonly made that images are only compared to
one uniq... | computer science |
2,996 | A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic
Extraction of Semantics, Formation of Integrated Concepts and Re-selection
Features for Ambiguity | cs.CV | Integration between biology and information science benefits both fields.
Many related models have been proposed, such as computational visual cognition
models, computational motor control models, integrations of both and so on. In
general, the robustness and precision of recognition is one of the key problems
for obje... | computer science |
2,997 | Shuffle and Learn: Unsupervised Learning using Temporal Order
Verification | cs.CV | In this paper, we present an approach for learning a visual representation
from the raw spatiotemporal signals in videos. Our representation is learned
without supervision from semantic labels. We formulate our method as an
unsupervised sequential verification task, i.e., we determine whether a
sequence of frames from ... | computer science |
2,998 | Look-ahead before you leap: end-to-end active recognition by forecasting
the effect of motion | cs.CV | Visual recognition systems mounted on autonomous moving agents face the
challenge of unconstrained data, but simultaneously have the opportunity to
improve their performance by moving to acquire new views of test data. In this
work, we first show how a recurrent neural network-based system may be trained
to perform end... | computer science |
2,999 | ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement
Learning | cs.LG | The recent advances in deep neural networks have led to effective
vision-based reinforcement learning methods that have been employed to obtain
human-level controllers in Atari 2600 games from pixel data. Atari 2600 games,
however, do not resemble real-world tasks since they involve non-realistic 2D
environments and th... | computer science |
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