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27,402 | Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval | cs.CV | Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. Howev... | computer science |
27,403 | Anisotropic-Scale Junction Detection and Matching for Indoor Images | cs.CV | Junctions play an important role in the characterization of local geometric
structures in images, the detection of which is a longstanding and challenging
task. Existing junction detectors usually focus on identifying the junction
locations and the orientations of the junction branches while ignoring their
scales; howe... | computer science |
27,404 | Segmented and Directional Impact Detection for Parked Vehicles using
Mobile Devices | cs.CV | Mutual usage of vehicles as well as car sharing became more and more
attractive during the last years. Especially in urban environments with limited
parking possibilities and a higher risk for traffic jams, car rentals and
sharing services may save time and money. But when renting a vehicle it could
already be damaged ... | computer science |
27,405 | SVDNet for Pedestrian Retrieval | cs.CV | This paper proposes the SVDNet for retrieval problems, with focus on the
application of person re-identification (re-ID). We view each weight vector
within a fully connected (FC) layer in a convolutional neuron network (CNN) as
a projection basis. It is observed that the weight vectors are usually highly
correlated. Th... | computer science |
27,406 | Learning Robust Hash Codes for Multiple Instance Image Retrieval | cs.CV | In this paper, for the first time, we introduce a multiple instance (MI) deep
hashing technique for learning discriminative hash codes with weak bag-level
supervision suited for large-scale retrieval. We learn such hash codes by
aggregating deeply learnt hierarchical representations across bag members
through a dedicat... | computer science |
27,407 | Towards Closing the Energy Gap Between HOG and CNN Features for Embedded
Vision | cs.CV | Computer vision enables a wide range of applications in robotics/drones,
self-driving cars, smart Internet of Things, and portable/wearable electronics.
For many of these applications, local embedded processing is preferred due to
privacy and/or latency concerns. Accordingly, energy-efficient embedded vision
hardware d... | computer science |
27,408 | Understanding Traffic Density from Large-Scale Web Camera Data | cs.CV | Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid indivi... | computer science |
27,409 | DropRegion Training of Inception Font Network for High-Performance
Chinese Font Recognition | cs.CV | Chinese font recognition (CFR) has gained significant attention in recent
years. However, due to the sparsity of labeled font samples and the structural
complexity of Chinese characters, CFR is still a challenging task. In this
paper, a DropRegion method is proposed to generate a large number of stochastic
variant font... | computer science |
27,410 | Need for Speed: A Benchmark for Higher Frame Rate Object Tracking | cs.CV | In this paper, we propose the first higher frame rate video dataset (called
Need for Speed - NfS) and benchmark for visual object tracking. The dataset
consists of 100 videos (380K frames) captured with now commonly available
higher frame rate (240 FPS) cameras from real world scenarios. All frames are
annotated with a... | computer science |
27,411 | Computer Aided Detection of Anemia-like Pallor | cs.CV | Paleness or pallor is a manifestation of blood loss or low hemoglobin
concentrations in the human blood that can be caused by pathologies such as
anemia. This work presents the first automated screening system that utilizes
pallor site images, segments, and extracts color and intensity-based features
for multi-class cl... | computer science |
27,412 | Semi-Supervised Deep Learning for Fully Convolutional Networks | cs.CV | Deep learning usually requires large amounts of labeled training data, but
annotating data is costly and tedious. The framework of semi-supervised
learning provides the means to use both labeled data and arbitrary amounts of
unlabeled data for training. Recently, semi-supervised deep learning has been
intensively studi... | computer science |
27,413 | Towards Diverse and Natural Image Descriptions via a Conditional GAN | cs.CV | Despite the substantial progress in recent years, the image captioning
techniques are still far from being perfect.Sentences produced by existing
methods, e.g. those based on RNNs, are often overly rigid and lacking in
variability. This issue is related to a learning principle widely used in
practice, that is, to maxim... | computer science |
27,414 | Hyperspectral Unmixing with Endmember Variability using Semi-supervised
Partial Membership Latent Dirichlet Allocation | cs.CV | A semi-supervised Partial Membership Latent Dirichlet Allocation approach is
developed for hyperspectral unmixing and endmember estimation while accounting
for spectral variability and spatial information. Partial Membership Latent
Dirichlet Allocation is an effective approach for spectral unmixing while
representing s... | computer science |
27,415 | TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals | cs.CV | Temporal Action Proposal (TAP) generation is an important problem, as fast
and accurate extraction of semantically important (e.g. human actions) segments
from untrimmed videos is an important step for large-scale video analysis. We
propose a novel Temporal Unit Regression Network (TURN) model. There are two
salient as... | computer science |
27,416 | Deformable Convolutional Networks | cs.CV | Convolutional neural networks (CNNs) are inherently limited to model
geometric transformations due to the fixed geometric structures in its building
modules. In this work, we introduce two new modules to enhance the
transformation modeling capacity of CNNs, namely, deformable convolution and
deformable RoI pooling. Bot... | computer science |
27,417 | Recurrent Models for Situation Recognition | cs.CV | This work proposes Recurrent Neural Network (RNN) models to predict
structured 'image situations' -- actions and noun entities fulfilling semantic
roles related to the action. In contrast to prior work relying on Conditional
Random Fields (CRFs), we use a specialized action prediction network followed
by an RNN for nou... | computer science |
27,418 | RoomNet: End-to-End Room Layout Estimation | cs.CV | This paper focuses on the task of room layout estimation from a monocular RGB
image. Prior works break the problem into two sub-tasks: semantic segmentation
of floor, walls, ceiling to produce layout hypotheses, followed by an iterative
optimization step to rank these hypotheses. In contrast, we adopt a more direct
for... | computer science |
27,419 | Towards Context-aware Interaction Recognition | cs.CV | Recognizing how objects interact with each other is a crucial task in visual
recognition. If we define the context of the interaction to be the objects
involved, then most current methods can be categorized as either: (i) training
a single classifier on the combination of the interaction and its context; or
(ii) aiming... | computer science |
27,420 | A Fast HOG Descriptor Using Lookup Table and Integral Image | cs.CV | The histogram of oriented gradients (HOG) is a widely used feature descriptor
in computer vision for the purpose of object detection. In the paper, a
modified HOG descriptor is described, it uses a lookup table and the method of
integral image to speed up the detection performance by a factor of 5~10. By
exploiting the... | computer science |
27,421 | Single image super-resolution using self-optimizing mask via
fractional-order gradient interpolation and reconstruction | cs.CV | Image super-resolution using self-optimizing mask via fractional-order
gradient interpolation and reconstruction aims to recover detailed information
from low-resolution images and reconstruct them into high-resolution images.
Due to the limited amount of data and information retrieved from low-resolution
images, it is... | computer science |
27,422 | PatternNet: Visual Pattern Mining with Deep Neural Network | cs.CV | Visual patterns represent the discernible regularity in the visual world.
They capture the essential nature of visual objects or scenes. Understanding
and modeling visual patterns is a fundamental problem in visual recognition
that has wide ranging applications. In this paper, we study the problem of
visual pattern min... | computer science |
27,423 | Weakly-supervised DCNN for RGB-D Object Recognition in Real-World
Applications Which Lack Large-scale Annotated Training Data | cs.CV | This paper addresses the problem of RGBD object recognition in real-world
applications, where large amounts of annotated training data are typically
unavailable. To overcome this problem, we propose a novel, weakly-supervised
learning architecture (DCNN-GPC) which combines parametric models (a pair of
Deep Convolutiona... | computer science |
27,424 | Zero-Shot Learning by Generating Pseudo Feature Representations | cs.CV | Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel
classes without any training instances. In this paper we present a simple but
high-performance ZSL approach by generating pseudo feature representations
(GPFR). Given the dataset of seen classes and side information of unseen
classes (e.g. attri... | computer science |
27,425 | Multilevel Context Representation for Improving Object Recognition | cs.CV | In this work, we propose the combined usage of low- and high-level blocks of
convolutional neural networks (CNNs) for improving object recognition. While
recent research focused on either propagating the context from all layers, e.g.
ResNet, (including the very low-level layers) or having multiple loss layers
(e.g. Goo... | computer science |
27,426 | TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial
Network | cs.CV | In this work, we present the Text Conditioned Auxiliary Classifier Generative
Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network
(GAN) for synthesizing images from their text descriptions. Former approaches
have tried to condition the generative process on the textual data; but allying
it to ... | computer science |
27,427 | A Fully-Automated Pipeline for Detection and Segmentation of Liver
Lesions and Pathological Lymph Nodes | cs.CV | We propose a fully-automated method for accurate and robust detection and
segmentation of potentially cancerous lesions found in the liver and in lymph
nodes. The process is performed in three steps, including organ detection,
lesion detection and lesion segmentation. Our method applies machine learning
techniques such... | computer science |
27,428 | Detecting Oriented Text in Natural Images by Linking Segments | cs.CV | Most state-of-the-art text detection methods are specific to horizontal Latin
text and are not fast enough for real-time applications. We introduce Segment
Linking (SegLink), an oriented text detection method. The main idea is to
decompose text into two locally detectable elements, namely segments and links.
A segment ... | computer science |
27,429 | Vision-based Real-Time Aerial Object Localization and Tracking for UAV
Sensing System | cs.CV | The paper focuses on the problem of vision-based obstacle detection and
tracking for unmanned aerial vehicle navigation. A real-time object
localization and tracking strategy from monocular image sequences is developed
by effectively integrating the object detection and tracking into a dynamic
Kalman model. At the dete... | computer science |
27,430 | Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media
Retrieval | cs.CV | This paper contributes a new large-scale dataset for weakly supervised
cross-media retrieval, named Twitter100k. Current datasets, such as Wikipedia,
NUS Wide and Flickr30k, have two major limitations. First, these datasets are
lacking in content diversity, i.e., only some pre-defined classes are covered.
Second, texts... | computer science |
27,431 | Second-order Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (CNNs) have been successfully applied to many
computer vision tasks, such as image classification. By performing linear
combinations and element-wise nonlinear operations, these networks can be
thought of as extracting solely first-order information from an input image. In
the past, howeve... | computer science |
27,432 | Arbitrary Style Transfer in Real-time with Adaptive Instance
Normalization | cs.CV | Gatys et al. recently introduced a neural algorithm that renders a content
image in the style of another image, achieving so-called style transfer.
However, their framework requires a slow iterative optimization process, which
limits its practical application. Fast approximations with feed-forward neural
networks have ... | computer science |
27,433 | Mask R-CNN | cs.CV | We present a conceptually simple, flexible, and general framework for object
instance segmentation. Our approach efficiently detects objects in an image
while simultaneously generating a high-quality segmentation mask for each
instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a
branch for predicti... | computer science |
27,434 | Fast Spectral Ranking for Similarity Search | cs.CV | Despite the success of deep learning on representing images for particular
object retrieval, recent studies show that the learned representations still
lie on manifolds in a high dimensional space. Therefore, nearest neighbor
search cannot be expected to be optimal for this task. Even if a nearest
neighbor graph is com... | computer science |
27,435 | Multi-style Generative Network for Real-time Transfer | cs.CV | Despite the rapid progress in style transfer, existing approaches using
feed-forward generative network for multi-style or arbitrary-style transfer are
usually compromised of image quality and model flexibility. We find it is
fundamentally difficult to achieve comprehensive style modeling using
1-dimensional style embe... | computer science |
27,436 | SORT: Second-Order Response Transform for Visual Recognition | cs.CV | In this paper, we reveal the importance and benefits of introducing
second-order operations into deep neural networks. We propose a novel approach
named Second-Order Response Transform (SORT), which appends element-wise
product transform to the linear sum of a two-branch network module. A direct
advantage of SORT is to... | computer science |
27,437 | Spatio-Temporal Facial Expression Recognition Using Convolutional Neural
Networks and Conditional Random Fields | cs.CV | Automated Facial Expression Recognition (FER) has been a challenging task for
decades. Many of the existing works use hand-crafted features such as LBP, HOG,
LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as
Support Vector Machines for expression recognition. These methods often require
rigorou... | computer science |
27,438 | Encouraging LSTMs to Anticipate Actions Very Early | cs.CV | In contrast to the widely studied problem of recognizing an action given a
complete sequence, action anticipation aims to identify the action from only
partially available videos. As such, it is therefore key to the success of
computer vision applications requiring to react as early as possible, such as
autonomous navi... | computer science |
27,439 | Deep generative-contrastive networks for facial expression recognition | cs.CV | As the expressive depth of an emotional face differs with individuals,
expressions, or situations, recognizing an expression using a single facial
image at a moment is difficult. One of the approaches to alleviate this
difficulty is using a video-based method that utilizes multiple frames to
extract temporal informatio... | computer science |
27,440 | Proposal Flow: Semantic Correspondences from Object Proposals | 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 |
27,441 | GP-GAN: Towards Realistic High-Resolution Image Blending | cs.CV | Recent advances in generative adversarial networks (GANs) have shown
promising potentials in conditional image generation. However, how to generate
high-resolution images remains an open problem. In this paper, we aim at
generating high-resolution well-blended images given composited copy-and-paste
ones, i.e. realistic... | computer science |
27,442 | Improving Person Re-identification by Attribute and Identity Learning | cs.CV | Person re-identification (re-ID) and attribute recognition share a common
target at the pedestrian description. Their difference consists in the
granularity. Attribute recognition focuses on local aspects of a person while
person re-ID usually extracts global representations. Considering their
similarity and difference... | computer science |
27,443 | On the use of convolutional neural networks for robust classification of
multiple fingerprint captures | cs.CV | Fingerprint classification is one of the most common approaches to accelerate
the identification in large databases of fingerprints. Fingerprints are grouped
into disjoint classes, so that an input fingerprint is compared only with those
belonging to the predicted class, reducing the penetration rate of the search.
The... | computer science |
27,444 | License Plate Detection and Recognition Using Deeply Learned
Convolutional Neural Networks | cs.CV | This work details Sighthounds fully automated license plate detection and
recognition system. The core technology of the system is built using a sequence
of deep Convolutional Neural Networks (CNNs) interlaced with accurate and
efficient algorithms. The CNNs are trained and fine-tuned so that they are
robust under diff... | computer science |
27,445 | Simple Online and Realtime Tracking with a Deep Association Metric | cs.CV | Simple Online and Realtime Tracking (SORT) is a pragmatic approach to
multiple object tracking with a focus on simple, effective algorithms. In this
paper, we integrate appearance information to improve the performance of SORT.
Due to this extension we are able to track objects through longer periods of
occlusions, eff... | computer science |
27,446 | IOD-CNN: Integrating Object Detection Networks for Event Recognition | cs.CV | Many previous methods have showed the importance of considering semantically
relevant objects for performing event recognition, yet none of the methods have
exploited the power of deep convolutional neural networks to directly integrate
relevant object information into a unified network. We present a novel unified
deep... | computer science |
27,447 | No Fuss Distance Metric Learning using Proxies | cs.CV | We address the problem of distance metric learning (DML), defined as learning
a distance consistent with a notion of semantic similarity. Traditionally, for
this problem supervision is expressed in the form of sets of points that follow
an ordinal relationship -- an anchor point $x$ is similar to a set of positive
poin... | computer science |
27,448 | PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action
Understanding | cs.CV | Despite the fact that many 3D human activity benchmarks being proposed, most
existing action datasets focus on the action recognition tasks for the
segmented videos. There is a lack of standard large-scale benchmarks,
especially for current popular data-hungry deep learning based methods. In this
paper, we introduce a ... | computer science |
27,449 | Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted
Transform Coefficients of Gradient Magnitudes | cs.CV | The detection of spatially-varying blur without having any information about
the blur type is a challenging task. In this paper, we propose a novel
effective approach to address the blur detection problem from a single image
without requiring any knowledge about the blur type, level, or camera settings.
Our approach co... | computer science |
27,450 | Knowledge Transfer for Melanoma Screening with Deep Learning | cs.CV | Knowledge transfer impacts the performance of deep learning -- the state of
the art for image classification tasks, including automated melanoma screening.
Deep learning's greed for large amounts of training data poses a challenge for
medical tasks, which we can alleviate by recycling knowledge from models
trained on d... | computer science |
27,451 | Deep Photo Style Transfer | cs.CV | This paper introduces a deep-learning approach to photographic style transfer
that handles a large variety of image content while faithfully transferring the
reference style. Our approach builds upon the recent work on painterly transfer
that separates style from the content of an image by considering different
layers ... | computer science |
27,452 | Video Frame Interpolation via Adaptive Convolution | cs.CV | Video frame interpolation typically involves two steps: motion estimation and
pixel synthesis. Such a two-step approach heavily depends on the quality of
motion estimation. This paper presents a robust video frame interpolation
method that combines these two steps into a single process. Specifically, our
method conside... | computer science |
27,453 | Joint Intermodal and Intramodal Label Transfers for Extremely Rare or
Unseen Classes | cs.CV | In this paper, we present a label transfer model from texts to images for
image classification tasks. The problem of image classification is often much
more challenging than text classification. On one hand, labeled text data is
more widely available than the labeled images for classification tasks. On the
other hand, ... | computer science |
27,454 | Deeply-Supervised CNN for Prostate Segmentation | cs.CV | Prostate segmentation from Magnetic Resonance (MR) images plays an important
role in image guided interven- tion. However, the lack of clear boundary
specifically at the apex and base, and huge variation of shape and texture
between the images from different patients make the task very challenging. To
overcome these pr... | computer science |
27,455 | Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D
vehicle analysis from monocular image | cs.CV | In this paper, we present a novel approach, called Deep MANTA (Deep
Many-Tasks), for many-task vehicle analysis from a given image. A robust
convolutional network is introduced for simultaneous vehicle detection, part
localization, visibility characterization and 3D dimension estimation. Its
architecture is based on a ... | computer science |
27,456 | An End-to-End Approach to Natural Language Object Retrieval via
Context-Aware Deep Reinforcement Learning | cs.CV | We propose an end-to-end approach to the natural language object retrieval
task, which localizes an object within an image according to a natural language
description, i.e., referring expression. Previous works divide this problem
into two independent stages: first, compute region proposals from the image
without the e... | computer science |
27,457 | Can you tell where in India I am from? Comparing humans and computers on
fine-grained race face classification | cs.CV | Faces form the basis for a rich variety of judgments in humans, yet the
underlying features remain poorly understood. Although fine-grained
distinctions within a race might more strongly constrain possible facial
features used by humans than in case of coarse categories such as race or
gender, such fine grained distinc... | computer science |
27,458 | Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in
Handwritten Manuscript Collections | cs.CV | In this paper, we approach the problem of segmentation-free query-by-string
word spotting for handwritten documents. In other words, we use methods
inspired from computer vision and machine learning to search for words in large
collections of digitized manuscripts. In particular, we are interested in
historical handwri... | computer science |
27,459 | Classifying Symmetrical Differences and Temporal Change in Mammography
Using Deep Neural Networks | cs.CV | We investigate the addition of symmetry and temporal context information to a
deep Convolutional Neural Network (CNN) with the purpose of detecting malignant
soft tissue lesions in mammography. We employ a simple linear mapping that
takes the location of a mass candidate and maps it to either the contra-lateral
or prio... | computer science |
27,460 | R-C3D: Region Convolutional 3D Network for Temporal Activity Detection | cs.CV | We address the problem of activity detection in continuous, untrimmed video
streams. This is a difficult task that requires extracting meaningful
spatio-temporal features to capture activities, accurately localizing the start
and end times of each activity. We introduce a new model, Region Convolutional
3D Network (R-C... | computer science |
27,461 | Cross-View Image Matching for Geo-localization in Urban Environments | cs.CV | In this paper, we address the problem of cross-view image geo-localization.
Specifically, we aim to estimate the GPS location of a query street view image
by finding the matching images in a reference database of geo-tagged bird's eye
view images, or vice versa. To this end, we present a new framework for
cross-view im... | computer science |
27,462 | Large Pose 3D Face Reconstruction from a Single Image via Direct
Volumetric CNN Regression | cs.CV | 3D face reconstruction is a fundamental Computer Vision problem of
extraordinary difficulty. Current systems often assume the availability of
multiple facial images (sometimes from the same subject) as input, and must
address a number of methodological challenges such as establishing dense
correspondences across large ... | computer science |
27,463 | Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning
for Hyperspectral Image Classification | cs.CV | This paper proposes a novel deep learning framework named
bidirectional-convolutional long short term memory (Bi-CLSTM) network to
automatically learn the spectral-spatial feature from hyperspectral images
(HSIs). In the network, the issue of spectral feature extraction is considered
as a sequence learning problem, and... | computer science |
27,464 | Planar Object Tracking in the Wild: A Benchmark | cs.CV | Planar object tracking plays an important role in computer vision and related
fields. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefull... | computer science |
27,465 | Recurrent Multimodal Interaction for Referring Image Segmentation | cs.CV | In this paper we are interested in the problem of image segmentation given
natural language descriptions, i.e. referring expressions. Existing works
tackle this problem by first modeling images and sentences independently and
then segment images by combining these two types of representations. We argue
that learning wo... | computer science |
27,466 | Robust SfM with Little Image Overlap | cs.CV | Usual Structure-from-Motion (SfM) techniques require at least trifocal
overlaps to calibrate cameras and reconstruct a scene. We consider here
scenarios of reduced image sets with little overlap, possibly as low as two
images at most seeing the same part of the scene. We propose a new method,
based on line coplanarity ... | computer science |
27,467 | Image-based Localization using Hourglass Networks | cs.CV | In this paper, we propose an encoder-decoder convolutional neural network
(CNN) architecture for estimating camera pose (orientation and location) from a
single RGB-image. The architecture has a hourglass shape consisting of a chain
of convolution and up-convolution layers followed by a regression part. The
up-convolut... | computer science |
27,468 | Weakly Supervised Object Localization Using Things and Stuff Transfer | cs.CV | We propose to help weakly supervised object localization for classes where
location annotations are not available, by transferring things and stuff
knowledge from a source set with available annotations. The source and target
classes might share similar appearance (e.g. bear fur is similar to cat fur) or
appear against... | computer science |
27,469 | Saliency-guided video classification via adaptively weighted learning | cs.CV | Video classification is productive in many practical applications, and the
recent deep learning has greatly improved its accuracy. However, existing works
often model video frames indiscriminately, but from the view of motion, video
frames can be decomposed into salient and non-salient areas naturally. Salient
and non-... | computer science |
27,470 | Is Second-order Information Helpful for Large-scale Visual Recognition? | cs.CV | By stacking layers of convolution and nonlinearity, convolutional networks
(ConvNets) effectively learn from low-level to high-level features and
discriminative representations. Since the end goal of large-scale recognition
is to delineate complex boundaries of thousands of classes, adequate
exploration of feature dist... | computer science |
27,471 | A Bag-of-Words Equivalent Recurrent Neural Network for Action
Recognition | cs.CV | The traditional bag-of-words approach has found a wide range of applications
in computer vision. The standard pipeline consists of a generation of a visual
vocabulary, a quantization of the features into histograms of visual words, and
a classification step for which usually a support vector machine in combination
with... | computer science |
27,472 | Quality Resilient Deep Neural Networks | cs.CV | We study deep neural networks for classification of images with quality
distortions. We first show that networks fine-tuned on distorted data greatly
outperform the original networks when tested on distorted data. However,
fine-tuned networks perform poorly on quality distortions that they have not
been trained for. We... | computer science |
27,473 | Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling | cs.CV | We present an approach for weakly supervised learning of human actions. Given
a set of videos and an ordered list of the occurring actions, the goal is to
infer start and end frames of the related action classes within the video and
to train the respective action classifiers without any need for hand labeled
frame boun... | computer science |
27,474 | Single Image Super-resolution via a Lightweight Residual Convolutional
Neural Network | cs.CV | Recent years have witnessed great success of convolutional neural network
(CNN) for various problems both in low and high level visions. Especially
noteworthy is the residual network which was originally proposed to handle
high-level vision problems and enjoys several merits. This paper aims to extend
the merits of res... | computer science |
27,475 | Semi-Automatic Segmentation and Ultrasonic Characterization of Solid
Breast Lesions | cs.CV | Characterization of breast lesions is an essential prerequisite to detect
breast cancer in an early stage. Automatic segmentation makes this
categorization method robust by freeing it from subjectivity and human error.
Both spectral and morphometric features are successfully used for
differentiating between benign and ... | computer science |
27,476 | View Adaptive Recurrent Neural Networks for High Performance Human
Action Recognition from Skeleton Data | cs.CV | Skeleton-based human action recognition has recently attracted increasing
attention due to the popularity of 3D skeleton data. One main challenge lies in
the large view variations in captured human actions. We propose a novel view
adaptation scheme to automatically regulate observation viewpoints during the
occurrence ... | computer science |
27,477 | Deep Direct Regression for Multi-Oriented Scene Text Detection | cs.CV | In this paper, we first provide a new perspective to divide existing high
performance object detection methods into direct and indirect regressions.
Direct regression performs boundary regression by predicting the offsets from a
given point, while indirect regression predicts the offsets from some bounding
box proposal... | computer science |
27,478 | Improving Classification by Improving Labelling: Introducing
Probabilistic Multi-Label Object Interaction Recognition | cs.CV | This work deviates from easy-to-define class boundaries for object
interactions. For the task of object interaction recognition, often captured
using an egocentric view, we show that semantic ambiguities in verbs and
recognising sub-interactions along with concurrent interactions result in
legitimate class overlaps (Fi... | computer science |
27,479 | Scalable Person Re-identification on Supervised Smoothed Manifold | cs.CV | Most existing person re-identification algorithms either extract robust
visual features or learn discriminative metrics for person images. However, the
underlying manifold which those images reside on is rarely investigated. That
raises a problem that the learned metric is not smooth with respect to the
local geometry ... | computer science |
27,480 | A Hybrid Deep Learning Approach for Texture Analysis | cs.CV | Texture classification is a problem that has various applications such as
remote sensing and forest species recognition. Solutions tend to be custom fit
to the dataset used but fails to generalize. The Convolutional Neural Network
(CNN) in combination with Support Vector Machine (SVM) form a robust selection
between po... | computer science |
27,481 | DeepVisage: Making face recognition simple yet with powerful
generalization skills | cs.CV | Face recognition (FR) methods report significant performance by adopting the
convolutional neural network (CNN) based learning methods. Although CNNs are
mostly trained by optimizing the softmax loss, the recent trend shows an
improvement of accuracy with different strategies, such as task-specific CNN
learning with di... | computer science |
27,482 | Object Region Mining with Adversarial Erasing: A Simple Classification
to Semantic Segmentation Approach | cs.CV | We investigate a principle way to progressively mine discriminative object
regions using classification networks to address the weakly-supervised semantic
segmentation problems. Classification networks are only responsive to small and
sparse discriminative regions from the object of interest, which deviates from
the re... | computer science |
27,483 | Medical Image Retrieval using Deep Convolutional Neural Network | cs.CV | With a widespread use of digital imaging data in hospitals, the size of
medical image repositories is increasing rapidly. This causes difficulty in
managing and querying these large databases leading to the need of content
based medical image retrieval (CBMIR) systems. A major challenge in CBMIR
systems is the semantic... | computer science |
27,484 | Content-Based Image Retrieval Based on Late Fusion of Binary and Local
Descriptors | cs.CV | One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce
the semantic gaps between low-level features and high-level semantic concepts.
In CBIR, the images are represented in the feature space and the performance of
CBIR depends on the type of selected feature representation. Late fusion also
known as... | computer science |
27,485 | Multi-stage Multi-recursive-input Fully Convolutional Networks for
Neuronal Boundary Detection | cs.CV | In the field of connectomics, neuroscientists seek to identify cortical
connectivity comprehensively. Neuronal boundary detection from the Electron
Microscopy (EM) images is often done to assist the automatic reconstruction of
neuronal circuit. But the segmentation of EM images is a challenging problem,
as it requires ... | computer science |
27,486 | Local Deep Neural Networks for Age and Gender Classification | cs.CV | Local deep neural networks have been recently introduced for gender
recognition. Although, they achieve very good performance they are very
computationally expensive to train. In this work, we introduce a simplified
version of local deep neural networks which significantly reduces the training
time. Instead of using hu... | computer science |
27,487 | Radiomics strategies for risk assessment of tumour failure in
head-and-neck cancer | cs.CV | Quantitative extraction of high-dimensional mineable data from medical images
is a process known as radiomics. Radiomics is foreseen as an essential
prognostic tool for cancer risk assessment and the quantification of
intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
tumour image intensity,... | computer science |
27,488 | Deep Residual Learning for Instrument Segmentation in Robotic Surgery | cs.CV | Detection, tracking, and pose estimation of surgical instruments are crucial
tasks for computer assistance during minimally invasive robotic surgery. In the
majority of cases, the first step is the automatic segmentation of surgical
tools. Prior work has focused on binary segmentation, where the objective is to
label e... | computer science |
27,489 | Adversarial Examples for Semantic Segmentation and Object Detection | cs.CV | It has been well demonstrated that adversarial examples, i.e., natural images
with visually imperceptible perturbations added, generally exist for deep
networks to fail on image classification. In this paper, we extend adversarial
examples to semantic segmentation and object detection which are much more
difficult. Our... | computer science |
27,490 | Temporal Non-Volume Preserving Approach to Facial Age-Progression and
Age-Invariant Face Recognition | cs.CV | Modeling the long-term facial aging process is extremely challenging due to
the presence of large and non-linear variations during the face development
stages. In order to efficiently address the problem, this work first decomposes
the aging process into multiple short-term stages. Then, a novel generative
probabilisti... | computer science |
27,491 | AMAT: Medial Axis Transform for Natural Images | cs.CV | We introduce Appearance-MAT (AMAT), a generalization of the medial axis
transform for natural images, that is framed as a weighted geometric set cover
problem. We make the following contributions: i) we extend previous medial
point detection methods for color images, by associating each medial point with
a local scale;... | computer science |
27,492 | More is Less: A More Complicated Network with Less Inference Complexity | cs.CV | In this paper, we present a novel and general network structure towards
accelerating the inference process of convolutional neural networks, which is
more complicated in network structure yet with less inference complexity. The
core idea is to equip each original convolutional layer with another low-cost
collaborative ... | computer science |
27,493 | Gaussian Processes with Context-Supported Priors for Active Object
Localization | cs.CV | We devise an algorithm using a Bayesian optimization framework in conjunction
with contextual visual data for the efficient localization of objects in still
images. Recent research has demonstrated substantial progress in object
localization and related tasks for computer vision. However, many current
state-of-the-art ... | computer science |
27,494 | Improving the Accuracy of the CogniLearn System for Cognitive Behavior
Assessment | cs.CV | HTKS is a game-like cognitive assessment method, designed for children
between four and eight years of age. During the HTKS assessment, a child
responds to a sequence of requests, such as "touch your head" or "touch your
toes". The cognitive challenge stems from the fact that the children are
instructed to interpret th... | computer science |
27,495 | Sketch-based Face Editing in Video Using Identity Deformation Transfer | cs.CV | We address the problem of using hand-drawn sketch to edit facial identity,
such as enlarging the shape or modifying the position of eyes or mouth, in the
whole video. This task is formulated as a 3D face model reconstruction and
deformation problem. We first introduce a two-stage real-time 3D face model
fitting schema ... | computer science |
27,496 | Structured Learning of Tree Potentials in CRF for Image Segmentation | cs.CV | We propose a new approach to image segmentation, which exploits the
advantages of both conditional random fields (CRFs) and decision trees. In the
literature, the potential functions of CRFs are mostly defined as a linear
combination of some pre-defined parametric models, and then methods like
structured support vector... | computer science |
27,497 | SCAN: Structure Correcting Adversarial Network for Organ Segmentation in
Chest X-rays | cs.CV | Chest X-ray (CXR) is one of the most commonly prescribed medical imaging
procedures, often with over 2-10x more scans than other imaging modalities such
as MRI, CT scan, and PET scans. These voluminous CXR scans place significant
workloads on radiologists and medical practitioners. Organ segmentation is a
crucial step ... | computer science |
27,498 | Person Re-Identification by Camera Correlation Aware Feature
Augmentation | cs.CV | The challenge of person re-identification (re-id) is to match individual
images of the same person captured by different non-overlapping camera views
against significant and unknown cross-view feature distortion. While a large
number of distance metric/subspace learning models have been developed for
re-id, the cross-v... | computer science |
27,499 | Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D
Cameras | cs.CV | Visual scene understanding is an important capability that enables robots to
purposefully act in their environment. In this paper, we propose a novel
approach to object-class segmentation from multiple RGB-D views using deep
learning. We train a deep neural network to predict object-class semantics that
is consistent f... | computer science |
27,500 | Transductive Zero-Shot Learning with a Self-training dictionary approach | cs.CV | As an important and challenging problem in computer vision, zero-shot
learning (ZSL) aims at automatically recognizing the instances from unseen
object classes without training data. To address this problem, ZSL is usually
carried out in the following two aspects: 1) capturing the domain distribution
connections betwee... | computer science |
27,501 | Transductive Zero-Shot Learning with Adaptive Structural Embedding | cs.CV | Zero-shot learning (ZSL) endows the computer vision system with the
inferential capability to recognize instances of a new category that has never
seen before. Two fundamental challenges in it are visual-semantic embedding and
domain adaptation in cross-modality learning and unseen class prediction steps,
respectively.... | computer science |
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