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25,702 | Online Supervised Hashing for Ever-Growing Datasets | cs.CV | Supervised hashing methods are widely-used for nearest neighbor search in
computer vision applications. Most state-of-the-art supervised hashing
approaches employ batch-learners. Unfortunately, batch-learning strategies can
be inefficient when confronted with large training datasets. Moreover, with
batch-learners, it i... | computer science |
25,703 | The Fast Bilateral Solver | cs.CV | We present the bilateral solver, a novel algorithm for edge-aware smoothing
that combines the flexibility and speed of simple filtering approaches with the
accuracy of domain-specific optimization algorithms. Our technique is capable
of matching or improving upon state-of-the-art results on several different
computer v... | computer science |
25,704 | Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs
and a Discriminatively Trained Domain Transform | cs.CV | Deep convolutional neural networks (CNNs) are the backbone of state-of-art
semantic image segmentation systems. Recent work has shown that complementing
CNNs with fully-connected conditional random fields (CRFs) can significantly
enhance their object localization accuracy, yet dense CRF inference is
computationally exp... | computer science |
25,705 | Attention to Scale: Scale-aware Semantic Image Segmentation | cs.CV | Incorporating multi-scale features in fully convolutional neural networks
(FCNs) has been a key element to achieving state-of-the-art performance on
semantic image segmentation. One common way to extract multi-scale features is
to feed multiple resized input images to a shared deep network and then merge
the resulting ... | computer science |
25,706 | Facial Expression Detection using Patch-based Eigen-face Isomap Networks | cs.CV | Automated facial expression detection problem pose two primary challenges
that include variations in expression and facial occlusions (glasses, beard,
mustache or face covers). In this paper we introduce a novel automated patch
creation technique that masks a particular region of interest in the face,
followed by Eigen... | computer science |
25,707 | A Directional Diffusion Algorithm for Inpainting | cs.CV | The problem of inpainting involves reconstructing the missing areas of an
image. Inpainting has many applications, such as reconstructing old damaged
photographs or removing obfuscations from images. In this paper we present the
directional diffusion algorithm for inpainting. Typical diffusion algorithms
are bad at pro... | computer science |
25,708 | God(s) Know(s): Developmental and Cross-Cultural Patterns in Children
Drawings | cs.CV | This paper introduces a novel approach to data analysis designed for the
needs of specialists in psychology of religion. We detect developmental and
cross-cultural patterns in children's drawings of God(s) and other supernatural
agents. We develop methods to objectively evaluate our empirical observations
of the drawin... | computer science |
25,709 | Hierarchical Recurrent Neural Encoder for Video Representation with
Application to Captioning | cs.CV | Recently, deep learning approach, especially deep Convolutional Neural
Networks (ConvNets), have achieved overwhelming accuracy with fast processing
speed for image classification. Incorporating temporal structure with deep
ConvNets for video representation becomes a fundamental problem for video
content analysis. In t... | computer science |
25,710 | A Continuous Max-Flow Approach to Cyclic Field Reconstruction | cs.CV | Reconstruction of an image from noisy data using Markov Random Field theory
has been explored by both the graph-cuts and continuous max-flow community in
the form of the Potts and Ishikawa models. However, neither model takes into
account the particular cyclic topology of specific intensity types such as the
hue in nat... | computer science |
25,711 | Piecewise Linear Activation Functions For More Efficient Deep Networks | cs.CV | This submission has been withdrawn by arXiv administrators because it is
intentionally incomplete, which is in violation of our policies. | computer science |
25,712 | Automatic Content-Aware Color and Tone Stylization | cs.CV | We introduce a new technique that automatically generates diverse, visually
compelling stylizations for a photograph in an unsupervised manner. We achieve
this by learning style ranking for a given input using a large photo collection
and selecting a diverse subset of matching styles for final style transfer. We
also p... | computer science |
25,713 | Shearlet-Based Detection of Flame Fronts | cs.CV | Identifying and characterizing flame fronts is the most common task in the
computer-assisted analysis of data obtained from imaging techniques such as
planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or
particle imaging velocimetry (PIV). We present a novel edge and ridge (line)
detection algo... | computer science |
25,714 | ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural
Networks | cs.CV | This paper aims to classify and locate objects accurately and efficiently,
without using bounding box annotations. It is challenging as objects in the
wild could appear at arbitrary locations and in different scales. In this
paper, we propose a novel classification architecture ProNet based on
convolutional neural netw... | computer science |
25,715 | Hand-Object Interaction and Precise Localization in Transitive Action
Recognition | cs.CV | Action recognition in still images has seen major improvement in recent years
due to advances in human pose estimation, object recognition and stronger
feature representations produced by deep neural networks. However, there are
still many cases in which performance remains far from that of humans. A major
difficulty a... | computer science |
25,716 | When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks | cs.CV | Since Convolutional Neural Networks (CNNs) have become the leading learning
paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based
classifiers have lost momentum in the community. This is because (1) such
algorithms cannot use CNN activations as input features; (2) they cannot be
used as final layer... | computer science |
25,717 | LLNet: A Deep Autoencoder Approach to Natural Low-light Image
Enhancement | cs.CV | In surveillance, monitoring and tactical reconnaissance, gathering the right
visual information from a dynamic environment and accurately processing such
data are essential ingredients to making informed decisions which determines
the success of an operation. Camera sensors are often cost-limited in ability
to clearly ... | computer science |
25,718 | Human Curation and Convnets: Powering Item-to-Item Recommendations on
Pinterest | cs.CV | This paper presents Pinterest Related Pins, an item-to-item recommendation
system that combines collaborative filtering with content-based ranking. We
demonstrate that signals derived from user curation, the activity of users
organizing content, are highly effective when used in conjunction with
content-based ranking. ... | computer science |
25,719 | Facial Landmark Detection with Tweaked Convolutional Neural Networks | cs.CV | We present a novel convolutional neural network (CNN) design for facial
landmark coordinate regression. We examine the intermediate features of a
standard CNN trained for landmark detection and show that features extracted
from later, more specialized layers capture rough landmark locations. This
provides a natural mea... | computer science |
25,720 | Newtonian Image Understanding: Unfolding the Dynamics of Objects in
Static Images | cs.CV | In this paper, we study the challenging problem of predicting the dynamics of
objects in static images. Given a query object in an image, our goal is to
provide a physical understanding of the object in terms of the forces acting
upon it and its long term motion as response to those forces. Direct and
explicit estimati... | computer science |
25,721 | Deep Gaussian Conditional Random Field Network: A Model-based Deep
Network for Discriminative Denoising | cs.CV | We propose a novel deep network architecture for image\\ denoising based on a
Gaussian Conditional Random Field (GCRF) model. In contrast to the existing
discriminative denoising methods that train a separate model for each noise
level, the proposed deep network explicitly models the input noise variance and
hence is c... | computer science |
25,722 | Basic Level Categorization Facilitates Visual Object Recognition | cs.CV | Recent advances in deep learning have led to significant progress in the
computer vision field, especially for visual object recognition tasks. The
features useful for object classification are learned by feed-forward deep
convolutional neural networks (CNNs) automatically, and they are shown to be
able to predict and ... | computer science |
25,723 | UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and
Tracking | cs.CV | In recent years, numerous effective multi-object tracking (MOT) methods are
developed because of the wide range of applications. Existing performance
evaluations of MOT methods usually separate the object tracking step from the
object detection step by using the same fixed object detection results for
comparisons. In t... | computer science |
25,724 | Unsupervised Learning of Edges | cs.CV | Data-driven approaches for edge detection have proven effective and achieve
top results on modern benchmarks. However, all current data-driven edge
detectors require manual supervision for training in the form of hand-labeled
region segments or object boundaries. Specifically, human annotators mark
semantically meaning... | computer science |
25,725 | Sequence to Sequence Learning for Optical Character Recognition | cs.CV | We propose an end-to-end recurrent encoder-decoder based sequence learning
approach for printed text Optical Character Recognition (OCR). In contrast to
present day existing state-of-art OCR solution which uses connectionist
temporal classification (CTC) output layer, our approach makes minimalistic
assumptions on the ... | computer science |
25,726 | DISC: Deep Image Saliency Computing via Progressive Representation
Learning | cs.CV | Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) ... | computer science |
25,727 | Structure Inference Machines: Recurrent Neural Networks for Analyzing
Relations in Group Activity Recognition | cs.CV | Rich semantic relations are important in a variety of visual recognition
problems. As a concrete example, group activity recognition involves the
interactions and relative spatial relations of a set of people in a scene.
State of the art recognition methods center on deep learning approaches for
training highly effecti... | computer science |
25,728 | An Adaptive Data Representation for Robust Point-Set Registration and
Merging | cs.CV | This paper presents a framework for rigid point-set registration and merging
using a robust continuous data representation. Our point-set representation is
constructed by training a one-class support vector machine with a Gaussian
radial basis function kernel and subsequently approximating the output function
with a Ga... | computer science |
25,729 | Volume-based Semantic Labeling with Signed Distance Functions | cs.CV | Research works on the two topics of Semantic Segmentation and SLAM
(Simultaneous Localization and Mapping) have been following separate tracks.
Here, we link them quite tightly by delineating a category label fusion
technique that allows for embedding semantic information into the dense map
created by a volume-based SL... | computer science |
25,730 | Learning to Assign Orientations to Feature Points | cs.CV | We show how to train a Convolutional Neural Network to assign a canonical
orientation to feature points given an image patch centered on the feature
point. Our method improves feature point matching upon the state-of-the art and
can be used in conjunction with any existing rotation sensitive descriptors. To
avoid the t... | computer science |
25,731 | Standard methods for inexpensive pollen loads authentication by means of
computer vision and machine learning | cs.CV | We present a complete methodology for authenticating local bee pollen against
fraudulent samples using image processing and machine learning techniques. The
proposed standard methods do not need expensive equipment such as advanced
microscopes and can be used for a preliminary fast rejection of unknown pollen
types. Th... | computer science |
25,732 | Learning Dense Convolutional Embeddings for Semantic Segmentation | cs.CV | This paper proposes a new deep convolutional neural network (DCNN)
architecture that learns pixel embeddings, such that pairwise distances between
the embeddings can be used to infer whether or not the pixels lie on the same
region. That is, for any two pixels on the same object, the embeddings are
trained to be simila... | computer science |
25,733 | Deep Reflectance Maps | cs.CV | Undoing the image formation process and therefore decomposing appearance into
its intrinsic properties is a challenging task due to the under-constraint
nature of this inverse problem. While significant progress has been made on
inferring shape, materials and illumination from images only, progress in an
unconstrained ... | computer science |
25,734 | Robust Face Alignment Using a Mixture of Invariant Experts | cs.CV | Face alignment, which is the task of finding the locations of a set of facial
landmark points in an image of a face, is useful in widespread application
areas. Face alignment is particularly challenging when there are large
variations in pose (in-plane and out-of-plane rotations) and facial expression.
To address this ... | computer science |
25,735 | Transductive Zero-Shot Action Recognition by Word-Vector Embedding | cs.CV | The number of categories for action recognition is growing rapidly and it has
become increasingly hard to label sufficient training data for learning
conventional models for all categories. Instead of collecting ever more data
and labelling them exhaustively for all categories, an attractive alternative
approach is zer... | computer science |
25,736 | Solving Jigsaw Puzzles with Linear Programming | cs.CV | We propose a novel Linear Program (LP) based formula- tion for solving jigsaw
puzzles. We formulate jigsaw solving as a set of successive global convex
relaxations of the stan- dard NP-hard formulation, that can describe both
jigsaws with pieces of unknown position and puzzles of unknown po- sition and
orientation. The... | computer science |
25,737 | Semantic Object Parsing with Local-Global Long Short-Term Memory | cs.CV | Semantic object parsing is a fundamental task for understanding objects in
detail in computer vision community, where incorporating multi-level contextual
information is critical for achieving such fine-grained pixel-level
recognition. Prior methods often leverage the contextual information through
post-processing pred... | computer science |
25,738 | Sequential Optimization for Efficient High-Quality Object Proposal
Generation | cs.CV | We are motivated by the need for a generic object proposal generation
algorithm which achieves good balance between object detection recall, proposal
localization quality and computational efficiency. We propose a novel object
proposal algorithm, BING++, which inherits the virtue of good computational
efficiency of BIN... | computer science |
25,739 | Zero-Shot Learning via Joint Latent Similarity Embedding | cs.CV | Zero-shot recognition (ZSR) deals with the problem of predicting class labels
for target domain instances based on source domain side information (e.g.
attributes) of unseen classes. We formulate ZSR as a binary prediction problem.
Our resulting classifier is class-independent. It takes an arbitrary pair of
source and ... | computer science |
25,740 | Reversible Recursive Instance-level Object Segmentation | cs.CV | In this work, we propose a novel Reversible Recursive Instance-level Object
Segmentation (R2-IOS) framework to address the challenging instance-level
object segmentation task. R2-IOS consists of a reversible proposal refinement
sub-network that predicts bounding box offsets for refining the object proposal
locations, a... | computer science |
25,741 | Learning Fine-grained Features via a CNN Tree for Large-scale
Classification | cs.CV | We propose a novel approach to enhance the discriminability of Convolutional
Neural Networks (CNN). The key idea is to build a tree structure that could
progressively learn fine-grained features to distinguish a subset of classes,
by learning features only among these classes. Such features are expected to be
more disc... | computer science |
25,742 | Jointly Learning Non-negative Projection and Dictionary with
Discriminative Graph Constraints for Classification | cs.CV | Sparse coding with dictionary learning (DL) has shown excellent
classification performance. Despite the considerable number of existing works,
how to obtain features on top of which dictionaries can be better learned
remains an open and interesting question. Many current prevailing DL methods
directly adopt well-perfor... | computer science |
25,743 | Implementation and comparative quantitative assessment of different
multispectral image pansharpening approches | cs.CV | In remote sensing, images acquired by various earth observation satellites
tend to have either a high spatial and low spectral resolution or vice versa.
Pansharpening is a technique which aims to improve spatial resolution of
multispectral image. The challenges involve in the pansharpening are not only
to improve the s... | computer science |
25,744 | Deep Neural Network for Real-Time Autonomous Indoor Navigation | cs.CV | Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many
challenges. One main reason is that GPS has limited precision in indoor
environments. The additional fact that MAVs are not able to carry heavy weight
or power consuming sensors, such as range finders, makes indoor autonomous
navigation a chall... | computer science |
25,745 | Uncovering Temporal Context for Video Question and Answering | cs.CV | In this work, we introduce Video Question Answering in temporal domain to
infer the past, describe the present and predict the future. We present an
encoder-decoder approach using Recurrent Neural Networks to learn temporal
structures of videos and introduce a dual-channel ranking loss to answer
multiple-choice questio... | computer science |
25,746 | Learning Mid-level Words on Riemannian Manifold for Action Recognition | cs.CV | Human action recognition remains a challenging task due to the various
sources of video data and large intra-class variations. It thus becomes one of
the key issues in recent research to explore effective and robust
representation to handle such challenges. In this paper, we propose a novel
representation approach by c... | computer science |
25,747 | Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression | cs.CV | Facial landmark localization plays an important role in face recognition and
analysis applications. In this paper, we give a brief introduction to a
coarse-to-fine pipeline with neural networks and sequential regression. First,
a global convolutional network is applied to the holistic facial image to give
an initial la... | computer science |
25,748 | Identification and Counting White Blood Cells and Red Blood Cells using
Image Processing Case Study of Leukemia | cs.CV | Leukemia is diagnosed with complete blood counts which is by calculating all
blood cells and compare the number of white blood cells (White Blood Cells /
WBC) and red blood cells (Red Blood Cells / RBC). Information obtained from a
complete blood count, has become a cornerstone in the hematology laboratory for
diagnost... | computer science |
25,749 | Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering | cs.CV | Scene parsing has attracted a lot of attention in computer vision. While
parametric models have proven effective for this task, they cannot easily
incorporate new training data. By contrast, nonparametric approaches, which
bypass any learning phase and directly transfer the labels from the training
data to the query im... | computer science |
25,750 | Handcrafted Local Features are Convolutional Neural Networks | cs.CV | Image and video classification research has made great progress through the
development of handcrafted local features and learning based features. These
two architectures were proposed roughly at the same time and have flourished at
overlapping stages of history. However, they are typically viewed as distinct
approache... | computer science |
25,751 | An Empirical Study of Recent Face Alignment Methods | cs.CV | The problem of face alignment has been intensively studied in the past years.
A large number of novel methods have been proposed and reported very good
performance on benchmark dataset such as 300W. However, the differences in the
experimental setting and evaluation metric, missing details in the description
of the met... | computer science |
25,752 | Proposal Flow | cs.CV | Finding image correspondences remains a challenging problem in the presence
of intra-class variations and large changes in scene layout.~Semantic flow
methods are designed to handle images depicting different instances of the same
object or scene category. We introduce a novel approach to semantic flow,
dubbed proposal... | computer science |
25,753 | Joint Training of Generic CNN-CRF Models with Stochastic Optimization | cs.CV | We propose a new CNN-CRF end-to-end learning framework, which is based on
joint stochastic optimization with respect to both Convolutional Neural Network
(CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient
descent is a standard technique for CNN training, it was not used for joint
models so f... | computer science |
25,754 | Understanding learned CNN features through Filter Decoding with
Substitution | cs.CV | In parallel with the success of CNNs to solve vision problems, there is a
growing interest in developing methodologies to understand and visualize the
internal representations of these networks. How the responses of a trained CNN
encode the visual information is a fundamental question both for computer and
human vision... | computer science |
25,755 | Nonlinear Local Metric Learning for Person Re-identification | cs.CV | Person re-identification aims at matching pedestrians observed from
non-overlapping camera views. Feature descriptor and metric learning are two
significant problems in person re-identification. A discriminative metric
learning method should be capable of exploiting complex nonlinear
transformations due to the large va... | computer science |
25,756 | Visualizing and Understanding Deep Texture Representations | cs.CV | A number of recent approaches have used deep convolutional neural networks
(CNNs) to build texture representations. Nevertheless, it is still unclear how
these models represent texture and invariances to categorical variations. This
work conducts a systematic evaluation of recent CNN-based texture descriptors
for recog... | computer science |
25,757 | Learning Expressionlets via Universal Manifold Model for Dynamic Facial
Expression Recognition | cs.CV | Facial expression is temporally dynamic event which can be decomposed into a
set of muscle motions occurring in different facial regions over various time
intervals. For dynamic expression recognition, two key issues, temporal
alignment and semantics-aware dynamic representation, must be taken into
account. In this pap... | computer science |
25,758 | Hierarchical Spatial Sum-Product Networks for Action Recognition in
Still Images | cs.CV | Recognizing actions from still images is popularly studied recently. In this
paper, we model an action class as a flexible number of spatial configurations
of body parts by proposing a new spatial SPN (Sum-Product Networks). First, we
discover a set of parts in image collections via unsupervised learning. Then,
our new... | computer science |
25,759 | Towards Predicting the Likeability of Fashion Images | cs.CV | In this paper, we propose a method for ranking fashion images to find the
ones which might be liked by more people. We collect two new datasets from
image sharing websites (Pinterest and Polyvore). We represent fashion images
based on attributes: semantic attributes and data-driven attributes. To learn
semantic attribu... | computer science |
25,760 | Super-Resolution with Deep Convolutional Sufficient Statistics | cs.CV | Inverse problems in image and audio, and super-resolution in particular, can
be seen as high-dimensional structured prediction problems, where the goal is
to characterize the conditional distribution of a high-resolution output given
its low-resolution corrupted observation. When the scaling ratio is small,
point estim... | computer science |
25,761 | Compositional Memory for Visual Question Answering | cs.CV | Visual Question Answering (VQA) emerges as one of the most fascinating topics
in computer vision recently. Many state of the art methods naively use holistic
visual features with language features into a Long Short-Term Memory (LSTM)
module, neglecting the sophisticated interaction between them. This coarse
modeling al... | computer science |
25,762 | Labeled pupils in the wild: A dataset for studying pupil detection in
unconstrained environments | cs.CV | We present labelled pupils in the wild (LPW), a novel dataset of 66
high-quality, high-speed eye region videos for the development and evaluation
of pupil detection algorithms. The videos in our dataset were recorded from 22
participants in everyday locations at about 95 FPS using a state-of-the-art
dark-pupil head-mou... | computer science |
25,763 | From Pose to Activity: Surveying Datasets and Introducing CONVERSE | cs.CV | We present a review on the current state of publicly available datasets
within the human action recognition community; highlighting the revival of pose
based methods and recent progress of understanding person-person interaction
modeling. We categorize datasets regarding several key properties for usage as
a benchmark ... | computer science |
25,764 | Particular object retrieval with integral max-pooling of CNN activations | cs.CV | Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some particu... | computer science |
25,765 | Collecting and Annotating the Large Continuous Action Dataset | cs.CV | We make available to the community a new dataset to support
action-recognition research. This dataset is different from prior datasets in
several key ways. It is significantly larger. It contains streaming video with
long segments containing multiple action occurrences that often overlap in
space and/or time. All actio... | computer science |
25,766 | ABC-CNN: An Attention Based Convolutional Neural Network for Visual
Question Answering | cs.CV | We propose a novel attention based deep learning architecture for visual
question answering task (VQA). Given an image and an image related natural
language question, VQA generates the natural language answer for the question.
Generating the correct answers requires the model's attention to focus on the
regions corresp... | computer science |
25,767 | Active Object Localization with Deep Reinforcement Learning | cs.CV | We present an active detection model for localizing objects in scenes. The
model is class-specific and allows an agent to focus attention on candidate
regions for identifying the correct location of a target object. This agent
learns to deform a bounding box using simple transformation actions, with the
goal of determi... | computer science |
25,768 | A Hierarchical Deep Temporal Model for Group Activity Recognition | cs.CV | In group activity recognition, the temporal dynamics of the whole activity
can be inferred based on the dynamics of the individual people representing the
activity. We build a deep model to capture these dynamics based on LSTM
(long-short term memory) models. To make use of these ob- servations, we
present a 2-stage de... | computer science |
25,769 | Compact Bilinear Pooling | cs.CV | Bilinear models has been shown to achieve impressive performance on a wide
range of visual tasks, such as semantic segmentation, fine grained recognition
and face recognition. However, bilinear features are high dimensional,
typically on the order of hundreds of thousands to a few million, which makes
them impractical ... | computer science |
25,770 | Structured Depth Prediction in Challenging Monocular Video Sequences | cs.CV | In this paper, we tackle the problem of estimating the depth of a scene from
a monocular video sequence. In particular, we handle challenging scenarios,
such as non-translational camera motion and dynamic scenes, where traditional
structure from motion and motion stereo methods do not apply. To this end, we
first study... | computer science |
25,771 | Quantitative Analysis of Particles Segregation | cs.CV | Segregation is a popular phenomenon. It has considerable effects on material
performance. To the author's knowledge, there is still no automated objective
quantitative indicator for segregation. In order to full fill this task,
segregation of particles is analyzed. Edges of the particles are extracted from
the digital ... | computer science |
25,772 | What Players do with the Ball: A Physically Constrained Interaction
Modeling | cs.CV | Tracking the ball is critical for video-based analysis of team sports.
However, it is difficult, especially in low-resolution images, due to the small
size of the ball, its speed that creates motion blur, and its often being
occluded by players. In this paper, we propose a generic and principled
approach to modeling th... | computer science |
25,773 | Automatically selecting inference algorithms for discrete energy
minimisation | cs.CV | Minimisation of discrete energies defined over factors is an important
problem in computer vision, and a vast number of MAP inference algorithms have
been proposed. Different inference algorithms perform better on factor graph
models (GMs) from different underlying problem classes, and in general it is
difficult to kno... | computer science |
25,774 | face anti-spoofing based on color texture analysis | cs.CV | Research on face spoofing detection has mainly been focused on analyzing the
luminance of the face images, hence discarding the chrominance information
which can be useful for discriminating fake faces from genuine ones. In this
work, we propose a new face anti-spoofing method based on color texture
analysis. We analyz... | computer science |
25,775 | Feature-based Attention in Convolutional Neural Networks | cs.CV | Convolutional neural networks (CNNs) have proven effective for image
processing tasks, such as object recognition and classification. Recently, CNNs
have been enhanced with concepts of attention, similar to those found in
biology. Much of this work on attention has focused on effective serial spatial
processing. In thi... | computer science |
25,776 | Bidirectional Warping of Active Appearance Model | cs.CV | Active Appearance Model (AAM) is a commonly used method for facial image
analysis with applications in face identification and facial expression
recognition. This paper proposes a new approach based on image alignment for
AAM fitting called bidirectional warping. Previous approaches warp either the
input image or the a... | computer science |
25,777 | WIDER FACE: A Face Detection Benchmark | cs.CV | Face detection is one of the most studied topics in the computer vision
community. Much of the progresses have been made by the availability of face
detection benchmark datasets. We show that there is a gap between current face
detection performance and the real world requirements. To facilitate future
face detection r... | computer science |
25,778 | A dense subgraph based algorithm for compact salient image region
detection | cs.CV | We present an algorithm for graph based saliency computation that utilizes
the underlying dense subgraphs in finding visually salient regions in an image.
To compute the salient regions, the model first obtains a saliency map using
random walks on a Markov chain. Next, k-dense subgraphs are detected to further
enhance ... | computer science |
25,779 | ElSe: Ellipse Selection for Robust Pupil Detection in Real-World
Environments | cs.CV | Fast and robust pupil detection is an essential prerequisite for video-based
eye-tracking in real-world settings. Several algorithms for image-based pupil
detection have been proposed, their applicability is mostly limited to
laboratory conditions. In realworld scenarios, automated pupil detection has to
face various c... | computer science |
25,780 | Towards Arbitrary-View Face Alignment by Recommendation Trees | cs.CV | Learning to simultaneously handle face alignment of arbitrary views, e.g.
frontal and profile views, appears to be more challenging than we thought. The
difficulties lay in i) accommodating the complex appearance-shape relations
exhibited in different views, and ii) encompassing the varying landmark point
sets due to s... | computer science |
25,781 | DeepCut: Joint Subset Partition and Labeling for Multi Person Pose
Estimation | cs.CV | This paper considers the task of articulated human pose estimation of
multiple people in real world images. We propose an approach that jointly
solves the tasks of detection and pose estimation: it infers the number of
persons in a scene, identifies occluded body parts, and disambiguates body
parts between people in cl... | computer science |
25,782 | Tracklet Association by Online Target-Specific Metric Learning and
Coherent Dynamics Estimation | cs.CV | In this paper, we present a novel method based on online target-specific
metric learning and coherent dynamics estimation for tracklet (track fragment)
association by network flow optimization in long-term multi-person tracking.
Our proposed framework aims to exploit appearance and motion cues to prevent
identity switc... | computer science |
25,783 | Personalizing Human Video Pose Estimation | cs.CV | We propose a personalized ConvNet pose estimator that automatically adapts
itself to the uniqueness of a person's appearance to improve pose estimation in
long videos. We make the following contributions: (i) we show that given a few
high-precision pose annotations, e.g. from a generic ConvNet pose estimator,
additiona... | computer science |
25,784 | Deep End2End Voxel2Voxel Prediction | cs.CV | Over the last few years deep learning methods have emerged as one of the most
prominent approaches for video analysis. However, so far their most successful
applications have been in the area of video classification and detection, i.e.,
problems involving the prediction of a single class label or a handful of
output va... | computer science |
25,785 | Direct Prediction of 3D Body Poses from Motion Compensated Sequences | cs.CV | We propose an efficient approach to exploiting motion information from
consecutive frames of a video sequence to recover the 3D pose of people.
Previous approaches typically compute candidate poses in individual frames and
then link them in a post-processing step to resolve ambiguities. By contrast,
we directly regress... | computer science |
25,786 | Multi-view 3D Models from Single Images with a Convolutional Network | cs.CV | We present a convolutional network capable of inferring a 3D representation
of a previously unseen object given a single image of this object. Concretely,
the network can predict an RGB image and a depth map of the object as seen from
an arbitrary view. Several of these depth maps fused together give a full point
cloud... | computer science |
25,787 | Semantic Diversity versus Visual Diversity in Visual Dictionaries | cs.CV | Visual dictionaries are a critical component for image
classification/retrieval systems based on the bag-of-visual-words (BoVW) model.
Dictionaries are usually learned without supervision from a training set of
images sampled from the collection of interest. However, for large,
general-purpose, dynamic image collection... | computer science |
25,788 | Superpixel Convolutional Networks using Bilateral Inceptions | cs.CV | In this paper we propose a CNN architecture for semantic image segmentation.
We introduce a new 'bilateral inception' module that can be inserted in
existing CNN architectures and performs bilateral filtering, at multiple
feature-scales, between superpixels in an image. The feature spaces for
bilateral filtering and ot... | computer science |
25,789 | Recognizing Activities of Daily Living with a Wrist-mounted Camera | cs.CV | We present a novel dataset and a novel algorithm for recognizing activities
of daily living (ADL) from a first-person wearable camera. Handled objects are
crucially important for egocentric ADL recognition. For specific examination of
objects related to users' actions separately from other objects in an
environment, ma... | computer science |
25,790 | The Unreasonable Effectiveness of Noisy Data for Fine-Grained
Recognition | cs.CV | Current approaches for fine-grained recognition do the following: First,
recruit experts to annotate a dataset of images, optionally also collecting
more structured data in the form of part annotations and bounding boxes.
Second, train a model utilizing this data. Toward the goal of solving
fine-grained recognition, we... | computer science |
25,791 | Ground-truth dataset and baseline evaluations for image base-detail
separation algorithms | cs.CV | Base-detail separation is a fundamental computer vision problem consisting of
modeling a smooth base layer with the coarse structures, and a detail layer
containing the texture-like structures. One of the challenges of estimating the
base is to preserve sharp boundaries between objects or parts to avoid halo
artifacts.... | computer science |
25,792 | Fidelity-Naturalness Evaluation of Single Image Super Resolution | cs.CV | We study the problem of evaluating super resolution methods. Traditional
evaluation methods usually judge the quality of super resolved images based on
a single measure of their difference with the original high resolution images.
In this paper, we proposed to use both fidelity (the difference with original
images) and... | computer science |
25,793 | TransCut: Transparent Object Segmentation from a Light-Field Image | cs.CV | The segmentation of transparent objects can be very useful in computer vision
applications. However, because they borrow texture from their background and
have a similar appearance to their surroundings, transparent objects are not
handled well by regular image segmentation methods. We propose a method that
overcomes t... | computer science |
25,794 | Convex Sparse Spectral Clustering: Single-view to Multi-view | cs.CV | Spectral Clustering (SC) is one of the most widely used methods for data
clustering. It first finds a low-dimensonal embedding $\U$ of data by computing
the eigenvectors of the normalized Laplacian matrix, and then performs k-means
on $\U^\top$ to get the final clustering result. In this work, we observe that,
in the i... | computer science |
25,795 | Screen Content Image Segmentation Using Sparse-Smooth Decomposition | cs.CV | Sparse decomposition has been extensively used for different applications
including signal compression and denoising and document analysis. In this
paper, sparse decomposition is used for image segmentation. The proposed
algorithm separates the background and foreground using a sparse-smooth
decomposition technique suc... | computer science |
25,796 | Semantic Segmentation of Colon Glands with Deep Convolutional Neural
Networks and Total Variation Segmentation | cs.CV | Segmentation of histopathology sections is an ubiquitous requirement in
digital pathology and due to the large variability of biological tissue,
machine learning techniques have shown superior performance over standard image
processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation
challenge, we prese... | computer science |
25,797 | Real-Time Anomaly Detection and Localization in Crowded Scenes | cs.CV | In this paper, we propose a method for real-time anomaly detection and
localization in crowded scenes. Each video is defined as a set of
non-overlapping cubic patches, and is described using two local and global
descriptors. These descriptors capture the video properties from different
aspects. By incorporating simple ... | computer science |
25,798 | Ask Me Anything: Free-form Visual Question Answering Based on Knowledge
from External Sources | cs.CV | We propose a method for visual question answering which combines an internal
representation of the content of an image with information extracted from a
general knowledge base to answer a broad range of image-based questions. This
allows more complex questions to be answered using the predominant neural
network-based a... | computer science |
25,799 | Learning High-level Prior with Convolutional Neural Networks for
Semantic Segmentation | cs.CV | This paper proposes a convolutional neural network that can fuse high-level
prior for semantic image segmentation. Motivated by humans' vision recognition
system, our key design is a three-layer generative structure consisting of
high-level coding, middle-level segmentation and low-level image to introduce
global prior... | computer science |
25,800 | SceneNet: Understanding Real World Indoor Scenes With Synthetic Data | cs.CV | Scene understanding is a prerequisite to many high level tasks for any
automated intelligent machine operating in real world environments. Recent
attempts with supervised learning have shown promise in this direction but also
highlighted the need for enormous quantity of supervised data --- performance
increases in pro... | computer science |
25,801 | Fine-grained pose prediction, normalization, and recognition | cs.CV | Pose variation and subtle differences in appearance are key challenges to
fine-grained classification. While deep networks have markedly improved general
recognition, many approaches to fine-grained recognition rely on anchoring
networks to parts for better accuracy. Identifying parts to find correspondence
discounts p... | computer science |
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