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28,402 | Hierarchical Label Inference for Video Classification | cs.CV | Videos are a rich source of high-dimensional structured data, with a wide
range of interacting components at varying levels of granularity. In order to
improve understanding of unconstrained internet videos, it is important to
consider the role of labels at separate levels of abstraction. In this paper,
we consider the... | computer science |
28,403 | Face Clustering: Representation and Pairwise Constraints | cs.CV | Clustering face images according to their identity has two important
applications: (i) grouping a collection of face images when no external labels
are associated with images, and (ii) indexing for efficient large scale face
retrieval. The clustering problem is composed of two key parts: face
representation and choice ... | computer science |
28,404 | Symplectomorphic registration with phase space regularization by entropy
spectrum pathways | cs.CV | The ability to register image data to a common coordinate system is a
critical feature of virtually all imaging studies that require multiple subject
analysis, combining single subject data from multiple modalities, or both.
However, in spite of the abundance of literature on the subject and the
existence of several va... | computer science |
28,405 | The Monkeytyping Solution to the YouTube-8M Video Understanding
Challenge | cs.CV | This article describes the final solution of team monkeytyping, who finished
in second place in the YouTube-8M video understanding challenge. The dataset
used in this challenge is a large-scale benchmark for multi-label video
classification. We extend the work in [1] and propose several improvements for
frame sequence ... | computer science |
28,406 | A Fully Trainable Network with RNN-based Pooling | cs.CV | Pooling is an important component in convolutional neural networks (CNNs) for
aggregating features and reducing computational burden. Compared with other
components such as convolutional layers and fully connected layers which are
completely learned from data, the pooling component is still handcrafted such
as max pool... | computer science |
28,407 | Dynamic Filters in Graph Convolutional Networks | cs.CV | Convolutional neural networks (CNNs) have massively impacted visual
recognition in 2D images, and are now ubiquitous in state-of-the-art
approaches. While CNNs naturally extend to other domains, such as audio and
video, where data is also organized in rectangular grids, they do not easily
generalize to other types of d... | computer science |
28,408 | Self-ensembling for visual domain adaptation | cs.CV | This paper explores the use of self-ensembling for visual domain adaptation
problems. Our technique is derived from the mean teacher variant (Tarvainen et
al., 2017) of temporal ensembling (Laine et al;, 2017), a technique that
achieved state of the art results in the area of semi-supervised learning. We
introduce a nu... | computer science |
28,409 | Perceptual Generative Adversarial Networks for Small Object Detection | cs.CV | Detecting small objects is notoriously challenging due to their low
resolution and noisy representation. Existing object detection pipelines
usually detect small objects through learning representations of all the
objects at multiple scales. However, the performance gain of such ad hoc
architectures is usually limited ... | computer science |
28,410 | Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and
FPGA Implementation | cs.CV | Rapid and low power computation of optical flow (OF) is potentially useful in
robotics. The dynamic vision sensor (DVS) event camera produces quick and
sparse output, and has high dynamic range, but conventional OF algorithms are
frame-based and cannot be directly used with event-based cameras. Previous DVS
OF methods ... | computer science |
28,411 | Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and
Text | cs.CV | The YouTube-8M video classification challenge requires teams to classify 0.7
million videos into one or more of 4,716 classes. In this Kaggle competition,
we placed in the top 3% out of 650 participants using released video and audio
features. Beyond that, we extend the original competition by including text
informatio... | computer science |
28,412 | Rotation Invariance Neural Network | cs.CV | Rotation invariance and translation invariance have great values in image
recognition tasks. In this paper, we bring a new architecture in convolutional
neural network (CNN) named cyclic convolutional layer to achieve rotation
invariance in 2-D symbol recognition. We can also get the position and
orientation of the 2-D... | computer science |
28,413 | Rethinking Atrous Convolution for Semantic Image Segmentation | cs.CV | In this work, we revisit atrous convolution, a powerful tool to explicitly
adjust filter's field-of-view as well as control the resolution of feature
responses computed by Deep Convolutional Neural Networks, in the application of
semantic image segmentation. To handle the problem of segmenting objects at
multiple scale... | computer science |
28,414 | Dimensionality Reduction using Similarity-induced Embeddings | cs.CV | The vast majority of Dimensionality Reduction (DR) techniques rely on
second-order statistics to define their optimization objective. Even though
this provides adequate results in most cases, it comes with several
shortcomings. The methods require carefully designed regularizers and they are
usually prone to outliers. ... | computer science |
28,415 | Tversky loss function for image segmentation using 3D fully
convolutional deep networks | cs.CV | Fully convolutional deep neural networks carry out excellent potential for
fast and accurate image segmentation. One of the main challenges in training
these networks is data imbalance, which is particularly problematic in medical
imaging applications such as lesion segmentation where the number of lesion
voxels is oft... | computer science |
28,416 | Using Deep Networks for Drone Detection | cs.CV | Drone detection is the problem of finding the smallest rectangle that
encloses the drone(s) in a video sequence. In this study, we propose a solution
using an end-to-end object detection model based on convolutional neural
networks. To solve the scarce data problem for training the network, we propose
an algorithm for ... | computer science |
28,417 | 3D Convolutional Neural Networks for Cross Audio-Visual Matching
Recognition | cs.CV | Audio-visual recognition (AVR) has been considered as a solution for speech
recognition tasks when the audio is corrupted, as well as a visual recognition
method used for speaker verification in multi-speaker scenarios. The approach
of AVR systems is to leverage the extracted information from one modality to
improve th... | computer science |
28,418 | An Entropy-based Pruning Method for CNN Compression | cs.CV | This paper aims to simultaneously accelerate and compress off-the-shelf CNN
models via filter pruning strategy. The importance of each filter is evaluated
by the proposed entropy-based method first. Then several unimportant filters
are discarded to get a smaller CNN model. Finally, fine-tuning is adopted to
recover its... | computer science |
28,419 | Histograms of Gaussian normal distribution for feature matching in
clutter scenes | cs.CV | 3D feature descriptor provide information between corresponding models and
scenes. 3D objection recognition in cluttered scenes, however, remains a
largely unsolved problem. Practical applications impose several challenges
which are not fully addressed by existing methods. Especially in cluttered
scenes there are many ... | computer science |
28,420 | Deep learning with spatiotemporal consistency for nerve segmentation in
ultrasound images | cs.CV | Ultrasound-Guided Regional Anesthesia (UGRA) has been gaining importance in
the last few years, offering numerous advantages over alternative methods of
nerve localization (neurostimulation or paraesthesia). However, nerve detection
is one of the most tasks that anaesthetists can encounter in the UGRA
procedure. Comput... | computer science |
28,421 | Pedestrian Prediction by Planning using Deep Neural Networks | cs.CV | Accurate traffic participant prediction is the prerequisite for collision
avoidance of autonomous vehicles. In this work, we predict pedestrians by
emulating their own motion planning. From online observations, we infer a
mixture density function for possible destinations. We use this result as the
goal states of a pla... | computer science |
28,422 | Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern
Recognition Applications | cs.CV | The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern... | computer science |
28,423 | Bayesian Joint Modelling for Object Localisation in Weakly Labelled
Images | cs.CV | We address the problem of localisation of objects as bounding boxes in images
and videos with weak labels. This weakly supervised object localisation problem
has been tackled in the past using discriminative models where each object
class is localised independently from other classes. In this paper, a novel
framework b... | computer science |
28,424 | Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging | cs.CV | Intra-operative measurements of tissue shape and multi/ hyperspectral
information have the potential to provide surgical guidance and decision making
support. We report an optical probe based system to combine sparse
hyperspectral measurements and spectrally-encoded structured lighting (SL) for
surface measurements. Th... | computer science |
28,425 | Satellite Imagery Feature Detection using Deep Convolutional Neural
Network: A Kaggle Competition | cs.CV | This paper describes our approach to the DSTL Satellite Imagery Feature
Detection challenge run by Kaggle. The primary goal of this challenge is
accurate semantic segmentation of different classes in satellite imagery. Our
approach is based on an adaptation of fully convolutional neural network for
multispectral data p... | computer science |
28,426 | Multi-Target Tracking in Multiple Non-Overlapping Cameras using
Constrained Dominant Sets | cs.CV | In this paper, a unified three-layer hierarchical approach for solving
tracking problems in multiple non-overlapping cameras is proposed. Given a
video and a set of detections (obtained by any person detector), we first solve
within-camera tracking employing the first two layers of our framework and,
then, in the third... | computer science |
28,427 | Low Resolution Face Recognition Using a Two-Branch Deep Convolutional
Neural Network Architecture | cs.CV | We propose a novel couple mappings method for low resolution face recognition
using deep convolutional neural networks (DCNNs). The proposed architecture
consists of two branches of DCNNs to map the high and low resolution face
images into a common space with nonlinear transformations. The branch
corresponding to trans... | computer science |
28,428 | Learning-based Ensemble Average Propagator Estimation | cs.CV | By capturing the anisotropic water diffusion in tissue, diffusion magnetic
resonance imaging (dMRI) provides a unique tool for noninvasively probing the
tissue microstructure and orientation in the human brain. The diffusion profile
can be described by the ensemble average propagator (EAP), which is inferred
from obser... | computer science |
28,429 | Multi-frame image super-resolution with fast upscaling technique | cs.CV | Multi-frame image super-resolution (MISR) aims to fuse information in
low-resolution (LR) image sequence to compose a high-resolution (HR) one, which
is applied extensively in many areas recently. Different with single image
super-resolution (SISR), sub-pixel transitions between multiple frames
introduce additional inf... | computer science |
28,430 | Using Artificial Tokens to Control Languages for Multilingual Image
Caption Generation | cs.CV | Recent work in computer vision has yielded impressive results in
automatically describing images with natural language. Most of these systems
generate captions in a sin- gle language, requiring multiple language-specific
models to build a multilingual captioning system. We propose a very simple
technique to build a sin... | computer science |
28,431 | Clustering-Based Quantisation for PDE-Based Image Compression | cs.CV | Finding optimal data for inpainting is a key problem in the context of
partial differential equation based image compression. The data that yields the
most accurate reconstruction is real-valued. Thus, quantisation models are
mandatory to allow an efficient encoding. These can also be understood as
challenging data clu... | computer science |
28,432 | Outlier Regularization for Vector Data and L21 Norm Robustness | cs.CV | In many real-world applications, data usually contain outliers. One popular
approach is to use L2,1 norm function as a robust error/loss function. However,
the robustness of L2,1 norm function is not well understood so far. In this
paper, we propose a new Vector Outlier Regularization (VOR) framework to
understand and ... | computer science |
28,433 | The Compressed Model of Residual CNDS | cs.CV | Convolutional neural networks have achieved a great success in the recent
years. Although, the way to maximize the performance of the convolutional
neural networks still in the beginning. Furthermore, the optimization of the
size and the time that need to train the convolutional neural networks is very
far away from re... | computer science |
28,434 | A comparative study of breast surface reconstruction for aesthetic
outcome assessment | cs.CV | Breast cancer is the most prevalent cancer type in women, and while its
survival rate is generally high the aesthetic outcome is an increasingly
important factor when evaluating different treatment alternatives. 3D scanning
and reconstruction techniques offer a flexible tool for building detailed and
accurate 3D breast... | computer science |
28,435 | Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple
Objects | cs.CV | In this paper we introduce Co-Fusion, a dense SLAM system that takes a live
stream of RGB-D images as input and segments the scene into different objects
(using either motion or semantic cues) while simultaneously tracking and
reconstructing their 3D shape in real time. We use a multiple model fitting
approach where ea... | computer science |
28,436 | Using Convolutional Neural Networks in Robots with Limited Computational
Resources: Detecting NAO Robots while Playing Soccer | cs.CV | The main goal of this paper is to analyze the general problem of using
Convolutional Neural Networks (CNNs) in robots with limited computational
capabilities, and to propose general design guidelines for their use. In
addition, two different CNN based NAO robot detectors that are able to run in
real-time while playing ... | computer science |
28,437 | Compact Tensor Pooling for Visual Question Answering | cs.CV | Performing high level cognitive tasks requires the integration of feature
maps with drastically different structure. In Visual Question Answering (VQA)
image descriptors have spatial structures, while lexical inputs inherently
follow a temporal sequence. The recently proposed Multimodal Compact Bilinear
pooling (MCB) f... | computer science |
28,438 | GPGPU Acceleration of the KAZE Image Feature Extraction Algorithm | cs.CV | The recently proposed open-source KAZE image feature detection and
description algorithm offers unprecedented performance in comparison to
conventional ones like SIFT and SURF as it relies on nonlinear scale spaces
instead of Gaussian linear scale spaces. The improved performance, however,
comes with a significant comp... | computer science |
28,439 | Saliency Guided End-to-End Learning for Weakly Supervised Object
Detection | cs.CV | Weakly supervised object detection (WSOD), which is the problem of learning
detectors using only image-level labels, has been attracting more and more
interest. However, this problem is quite challenging due to the lack of
location supervision. To address this issue, this paper integrates saliency
into a deep architect... | computer science |
28,440 | Object Detection Using Deep CNNs Trained on Synthetic Images | cs.CV | The need for large annotated image datasets for training Convolutional Neural
Networks (CNNs) has been a significant impediment for their adoption in
computer vision applications. We show that with transfer learning an effective
object detector can be trained almost entirely on synthetically rendered
datasets. We apply... | computer science |
28,441 | GM-Net: Learning Features with More Efficiency | cs.CV | Deep Convolutional Neural Networks (CNNs) are capable of learning
unprecedentedly effective features from images. Some researchers have struggled
to enhance the parameters' efficiency using grouped convolution. However, the
relation between the optimal number of convolutional groups and the recognition
performance rema... | computer science |
28,442 | Learnable pooling with Context Gating for video classification | cs.CV | Current methods for video analysis often extract frame-level features using
pre-trained convolutional neural networks (CNNs). Such features are then
aggregated over time e.g., by simple temporal averaging or more sophisticated
recurrent neural networks such as long short-term memory (LSTM) or gated
recurrent units (GRU... | computer science |
28,443 | Class-specific image denoising using importance sampling | cs.CV | In this paper, we propose a new image denoising method, tailored to specific
classes of images, assuming that a dataset of clean images of the same class is
available. Similarly to the non-local means (NLM) algorithm, the proposed
method computes a weighted average of non-local patches, which we interpret
under the imp... | computer science |
28,444 | Graphcut Texture Synthesis for Single-Image Superresolution | cs.CV | Texture synthesis has proven successful at imitating a wide variety of
textures. Adding additional constraints (in the form of a low-resolution
version of the texture to be synthesized) makes it possible to use texture
synthesis methods for texture superresolution. | computer science |
28,445 | Scalable Online Convolutional Sparse Coding | cs.CV | Convolutional sparse coding (CSC) improves sparse coding by learning a
shift-invariant dictionary from the data. However, existing CSC algorithms
operate in the batch mode and are expensive, in terms of both space and time,
on large datasets. In this paper, we alleviate these problems by using online
learning. The key ... | computer science |
28,446 | Two-Stream Convolutional Networks for Dynamic Texture Synthesis | cs.CV | We introduce a two-stream model for dynamic texture synthesis. Our model is
based on pre-trained convolutional networks (ConvNets) that target two
independent tasks: (i) object recognition, and (ii) optical flow prediction.
Given an input dynamic texture, statistics of filter responses from the object
recognition ConvN... | computer science |
28,447 | Uncertainty-Aware Organ Classification for Surgical Data Science
Applications in Laparoscopy | cs.CV | Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera pr... | computer science |
28,448 | Personalized Automatic Estimation of Self-reported Pain Intensity from
Facial Expressions | cs.CV | Pain is a personal, subjective experience that is commonly evaluated through
visual analog scales (VAS). While this is often convenient and useful,
automatic pain detection systems can reduce pain score acquisition efforts in
large-scale studies by estimating it directly from the participants' facial
expressions. In th... | computer science |
28,449 | Comparison of Time-Frequency Representations for Environmental Sound
Classification using Convolutional Neural Networks | cs.CV | Recent successful applications of convolutional neural networks (CNNs) to
audio classification and speech recognition have motivated the search for
better input representations for more efficient training. Visual displays of an
audio signal, through various time-frequency representations such as
spectrograms offer a ri... | computer science |
28,450 | A Novel VHR Image Change Detection Algorithm Based on Image Fusion and
Fuzzy C-Means Clustering | cs.CV | This thesis describes a study to perform change detection on Very High
Resolution satellite images using image fusion based on 2D Discrete Wavelet
Transform and Fuzzy C-Means clustering algorithm. Multiple other methods are
also quantitatively and qualitatively compared in this study. | computer science |
28,451 | Synthesis of Near-regular Natural Textures | cs.CV | Texture synthesis is widely used in the field of computer graphics, vision,
and image processing. In the present paper, a texture synthesis algorithm is
proposed for near-regular natural textures with the help of a representative
periodic pattern extracted from the input textures using distance matching
function. Local... | computer science |
28,452 | A Self-Adaptive Proposal Model for Temporal Action Detection based on
Reinforcement Learning | cs.CV | Existing action detection algorithms usually generate action proposals
through an extensive search over the video at multiple temporal scales, which
brings about huge computational overhead and deviates from the human perception
procedure. We argue that the process of detecting actions should be naturally
one of observ... | computer science |
28,453 | Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet
Decomposition | cs.CV | Tissue oxygenation and perfusion can be an indicator for organ viability
during minimally invasive surgery, for example allowing real-time assessment of
tissue perfusion and oxygen saturation. Multispectral imaging is an optical
modality that can inspect tissue perfusion in wide field images without
contact. In this pa... | computer science |
28,454 | A Computer Vision Pipeline for Automated Determination of Cardiac
Structure and Function and Detection of Disease by Two-Dimensional
Echocardiography | cs.CV | Automated cardiac image interpretation has the potential to transform
clinical practice in multiple ways including enabling low-cost serial
assessment of cardiac function in the primary care and rural setting. We
hypothesized that advances in computer vision could enable building a fully
automated, scalable analysis pi... | computer science |
28,455 | Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans | cs.CV | Automatic segmentation of an organ and its cystic region is a prerequisite of
computer-aided diagnosis. In this paper, we focus on pancreatic cyst
segmentation in abdominal CT scan. This task is important and very useful in
clinical practice yet challenging due to the low contrast in boundary, the
variability in locati... | computer science |
28,456 | Tracking Single-Cells in Overcrowded Bacterial Colonies | cs.CV | Cell tracking enables data extraction from time-lapse "cell movies" and
promotes modeling biological processes at the single-cell level. We introduce a
new fully automated computational strategy to track accurately cells across
frames in time-lapse movies. Our method is based on a dynamic neighborhoods
formation and ma... | computer science |
28,457 | Fine-Grained Categorization via CNN-Based Automatic Extraction and
Integration of Object-Level and Part-Level Features | cs.CV | Fine-grained categorization can benefit from part-based features which reveal
subtle visual differences between object categories. Handcrafted features have
been widely used for part detection and classification. Although a recent trend
seeks to learn such features automatically using powerful deep learning models
such... | computer science |
28,458 | Learning Spatial-Aware Regressions for Visual Tracking | cs.CV | In this paper, we analyze the spatial information of deep features, and
propose two complementary regressions for robust visual tracking. First, we
propose a kernelized ridge regression model wherein the kernel value is defined
as the weighted sum of similarity scores of all pairs of patches between two
samples. We sho... | computer science |
28,459 | Fractal dimension analysis for automatic morphological galaxy
classification | cs.CV | In this report we present experimental results using
\emph{Haussdorf-Besicovich} fractal dimension for performing morphological
galaxy classification. The fractal dimension is a topological, structural and
spatial property that give us information about the space were an object lives.
We have calculated the fractal dim... | computer science |
28,460 | Deep Hashing Network for Unsupervised Domain Adaptation | cs.CV | In recent years, deep neural networks have emerged as a dominant machine
learning tool for a wide variety of application domains. However, training a
deep neural network requires a large amount of labeled data, which is an
expensive process in terms of time, labor and human expertise. Domain
adaptation or transfer lear... | computer science |
28,461 | Nonlinear Embedding Transform for Unsupervised Domain Adaptation | cs.CV | The problem of domain adaptation (DA) deals with adapting classifier models
trained on one data distribution to different data distributions. In this
paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised DA
by combining domain alignment along with similarity-based embedding. We also
introduce a v... | computer science |
28,462 | Coupled Support Vector Machines for Supervised Domain Adaptation | cs.CV | Popular domain adaptation (DA) techniques learn a classifier for the target
domain by sampling relevant data points from the source and combining it with
the target data. We present a Support Vector Machine (SVM) based supervised DA
technique, where the similarity between source and target domains is modeled as
the sim... | computer science |
28,463 | Multiresolution Match Kernels for Gesture Video Classification | cs.CV | The emergence of depth imaging technologies like the Microsoft Kinect has
renewed interest in computational methods for gesture classification based on
videos. For several years now, researchers have used the Bag-of-Features (BoF)
as a primary method for generation of feature vectors from video data for
recognition of ... | computer science |
28,464 | Listen to Your Face: Inferring Facial Action Units from Audio Channel | cs.CV | Extensive efforts have been devoted to recognizing facial action units (AUs).
However, it is still challenging to recognize AUs from spontaneous facial
displays especially when they are accompanied with speech. Different from all
prior work that utilized visual observations for facial AU recognition, this
paper present... | computer science |
28,465 | Sampling Matters in Deep Embedding Learning | cs.CV | Deep embeddings answer one simple question: How similar are two images?
Learning these embeddings is the bedrock of verification, zero-shot learning,
and visual search. The most prominent approaches optimize a deep convolutional
network with a suitable loss function, such as contrastive loss or triplet
loss. While a ri... | computer science |
28,466 | Joint Prediction of Depths, Normals and Surface Curvature from RGB
Images using CNNs | cs.CV | Understanding the 3D structure of a scene is of vital importance, when it
comes to developing fully autonomous robots. To this end, we present a novel
deep learning based framework that estimates depth, surface normals and surface
curvature by only using a single RGB image. To the best of our knowledge this
is the firs... | computer science |
28,467 | Computer-aided implant design for the restoration of cranial defects | cs.CV | Patient-specific cranial implants are important and necessary in the surgery
of cranial defect restoration. However, traditional methods of manual design of
cranial implants are complicated and time-consuming. Our purpose is to develop
a novel software named EasyCrania to design the cranial implants conveniently
and ef... | computer science |
28,468 | Training Adversarial Discriminators for Cross-channel Abnormal Event
Detection in Crowds | cs.CV | Abnormal crowd behaviour detection attracts a large interest due to its
importance in video surveillance scenarios. However, the ambiguity and the lack
of sufficient "abnormal" ground truth data makes end-to-end training of large
deep networks hard in this domain. In this paper we propose to use Generative
Adversarial ... | computer science |
28,469 | On Detection of Faint Edges in Noisy Images | cs.CV | A fundamental question for edge detection in noisy images is how faint can an
edge be and still be detected. In this paper we offer a formalism to study this
question and subsequently introduce computationally efficient multiscale edge
detection algorithms designed to detect faint edges in noisy images. In our
formalis... | computer science |
28,470 | Fundamental Matrix Estimation: A Study of Error Criteria | cs.CV | The fundamental matrix (FM) describes the geometric relations that exist
between two images of the same scene. Different error criteria are used for
estimating FMs from an input set of correspondences. In this paper, the
accuracy and efficiency aspects of the different error criteria were studied.
We mathematically and... | computer science |
28,471 | Deep Mixture of Diverse Experts for Large-Scale Visual Recognition | cs.CV | In this paper, a deep mixture of diverse experts algorithm is developed for
seamlessly combining a set of base deep CNNs (convolutional neural networks)
with diverse outputs (task spaces), e.g., such base deep CNNs are trained to
recognize different subsets of tens of thousands of atomic object classes.
First, a two-la... | computer science |
28,472 | Encoding Video and Label Priors for Multi-label Video Classification on
YouTube-8M dataset | cs.CV | YouTube-8M is the largest video dataset for multi-label video classification.
In order to tackle the multi-label classification on this challenging dataset,
it is necessary to solve several issues such as temporal modeling of videos,
label imbalances, and correlations between labels. We develop a deep neural
network mo... | computer science |
28,473 | Irregular Convolutional Neural Networks | cs.CV | Convolutional kernels are basic and vital components of deep Convolutional
Neural Networks (CNN). In this paper, we equip convolutional kernels with shape
attributes to generate the deep Irregular Convolutional Neural Networks (ICNN).
Compared to traditional CNN applying regular convolutional kernels like
${3\times3}$,... | computer science |
28,474 | Decomposing Motion and Content for Natural Video Sequence Prediction | cs.CV | We propose a deep neural network for the prediction of future frames in
natural video sequences. To effectively handle complex evolution of pixels in
videos, we propose to decompose the motion and content, two key components
generating dynamics in videos. Our model is built upon the Encoder-Decoder
Convolutional Neural... | computer science |
28,475 | Efficient and accurate monitoring of the depth information in a Wireless
Multimedia Sensor Network based surveillance | cs.CV | Wireless Multimedia Sensor Network (WMSN) is a promising technology capturing
rich multimedia data like audio and video, which can be useful to monitor an
environment under surveillance. However, many scenarios in real time monitoring
requires 3D depth information. In this research work, we propose to use the
disparity... | computer science |
28,476 | FReLU: Flexible Rectified Linear Units for Improving Convolutional
Neural Networks | cs.CV | Rectified linear unit (ReLU) is a widely used activation function for deep
convolutional neural networks. However, because of the zero-hard rectification,
ReLU networks miss the benefits from negative values. In this paper, we propose
a novel activation function called \emph{flexible rectified linear unit
(FReLU)} to f... | computer science |
28,477 | Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych | cs.CV | In recent years a growing interest on action recognition is observed,
including detection of fall accident for the elderly. However, despite many
efforts undertaken, the existing technology is not widely used by elderly,
mainly because of its flaws like low precision, large number of false alarms,
inadequate privacy pr... | computer science |
28,478 | Scalable multimodal convolutional networks for brain tumour segmentation | cs.CV | Brain tumour segmentation plays a key role in computer-assisted surgery. Deep
neural networks have increased the accuracy of automatic segmentation
significantly, however these models tend to generalise poorly to different
imaging modalities than those for which they have been designed, thereby
limiting their applicati... | computer science |
28,479 | ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical
Tools | cs.CV | Real-time tool segmentation from endoscopic videos is an essential part of
many computer-assisted robotic surgical systems and of critical importance in
robotic surgical data science. We propose two novel deep learning architectures
for automatic segmentation of non-rigid surgical instruments. Both methods take
advanta... | computer science |
28,480 | Photometric Stereo by Hemispherical Metric Embedding | cs.CV | Photometric Stereo methods seek to reconstruct the 3d shape of an object from
motionless images obtained with varying illumination. Most existing methods
solve a restricted problem where the physical reflectance model, such as
Lambertian reflectance, is known in advance. In contrast, we do not restrict
ourselves to a s... | computer science |
28,481 | Robust Video-Based Eye Tracking Using Recursive Estimation of Pupil
Characteristics | cs.CV | Video-based eye tracking is a valuable technique in various research fields.
Numerous open-source eye tracking algorithms have been developed in recent
years, primarily designed for general application with many different camera
types. These algorithms do not, however, capitalize on the high frame rate of
eye tracking ... | computer science |
28,482 | End-to-end Learning of Image based Lane-Change Decision | cs.CV | We propose an image based end-to-end learning framework that helps
lane-change decisions for human drivers and autonomous vehicles. The proposed
system, Safe Lane-Change Aid Network (SLCAN), trains a deep convolutional
neural network to classify the status of adjacent lanes from rear view images
acquired by cameras mou... | computer science |
28,483 | YoTube: Searching Action Proposal via Recurrent and Static Regression
Networks | cs.CV | In this paper, we present YoTube-a novel network fusion framework for
searching action proposals in untrimmed videos, where each action proposal
corresponds to a spatialtemporal video tube that potentially locates one human
action. Our method consists of a recurrent YoTube detector and a static YoTube
detector, where t... | computer science |
28,484 | Multi-level SVM Based CAD Tool for Classifying Structural MRIs | cs.CV | The revolutionary developments in the field of supervised machine learning
have paved way to the development of CAD tools for assisting doctors in
diagnosis. Recently, the former has been employed in the prediction of
neurological disorders such as Alzheimer's disease. We propose a CAD (Computer
Aided Diagnosis tool fo... | computer science |
28,485 | Few-Example Object Detection with Model Communication | cs.CV | In this paper, we study object detection using a large pool of unlabeled
images and only a few labeled images per category, named "few-example object
detection". The key challenge consists in generating trustworthy training
samples as many as possible from the pool. Using few training examples as
seeds, our method iter... | computer science |
28,486 | Deep Semantics-Aware Photo Adjustment | cs.CV | Automatic photo adjustment is to mimic the photo retouching style of
professional photographers and automatically adjust photos to the learned
style. There have been many attempts to model the tone and the color adjustment
globally with low-level color statistics. Also, spatially varying photo
adjustment methods have b... | computer science |
28,487 | Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network
with Trust Gates | cs.CV | Skeleton-based human action recognition has attracted a lot of research
attention during the past few years. Recent works attempted to utilize
recurrent neural networks to model the temporal dependencies between the 3D
positional configurations of human body joints for better analysis of human
activities in the skeleta... | computer science |
28,488 | Semantically Informed Multiview Surface Refinement | cs.CV | We present a method to jointly refine the geometry and semantic segmentation
of 3D surface meshes. Our method alternates between updating the shape and the
semantic labels. In the geometry refinement step, the mesh is deformed with
variational energy minimization, such that it simultaneously maximizes
photo-consistency... | computer science |
28,489 | Learning to Map Vehicles into Bird's Eye View | cs.CV | Awareness of the road scene is an essential component for both autonomous
vehicles and Advances Driver Assistance Systems and is gaining importance both
for the academia and car companies. This paper presents a way to learn a
semantic-aware transformation which maps detections from a dashboard camera
view onto a broade... | computer science |
28,490 | Paying More Attention to Saliency: Image Captioning with Saliency and
Context Attention | cs.CV | Image captioning has been recently gaining a lot of attention thanks to the
impressive achievements shown by deep captioning architectures, which combine
Convolutional Neural Networks to extract image representations, and Recurrent
Neural Networks to generate the corresponding captions. At the same time, a
significant ... | computer science |
28,491 | Deep Network Flow for Multi-Object Tracking | cs.CV | Data association problems are an important component of many computer vision
applications, with multi-object tracking being one of the most prominent
examples. A typical approach to data association involves finding a graph
matching or network flow that minimizes a sum of pairwise association costs,
which are often eit... | computer science |
28,492 | Illuminating Pedestrians via Simultaneous Detection & Segmentation | cs.CV | Pedestrian detection is a critical problem in computer vision with
significant impact on safety in urban autonomous driving. In this work, we
explore how semantic segmentation can be used to boost pedestrian detection
accuracy while having little to no impact on network efficiency. We propose a
segmentation infusion ne... | computer science |
28,493 | Detecting Small Signs from Large Images | cs.CV | In the past decade, Convolutional Neural Networks (CNNs) have been
demonstrated successful for object detections. However, the size of network
input is limited by the amount of memory available on GPUs. Moreover,
performance degrades when detecting small objects. To alleviate the memory
usage and improve the performanc... | computer science |
28,494 | Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification | cs.CV | Sonar imaging has seen vast improvements over the last few decades due in
part to advances in synthetic aperture Sonar (SAS). Sophisticated
classification techniques can now be used in Sonar automatic target recognition
(ATR) to locate mines and other threatening objects. Among the most promising
of these methods is sp... | computer science |
28,495 | Do Deep Neural Networks Suffer from Crowding? | cs.CV | Crowding is a visual effect suffered by humans, in which an object that can
be recognized in isolation can no longer be recognized when other objects,
called flankers, are placed close to it. In this work, we study the effect of
crowding in artificial Deep Neural Networks for object recognition. We analyze
both standar... | computer science |
28,496 | Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal
Smoothness based Solution | cs.CV | This paper proposes a simple spatial-temporal smoothness based method for
solving dense non-rigid structure-from-motion (NRSfM). First, we revisit the
temporal smoothness and demonstrate that it can be extended to dense case
directly. Second, we propose to exploit the spatial smoothness by resorting to
the Laplacian of... | computer science |
28,497 | A Unified approach for Conventional Zero-shot, Generalized Zero-shot and
Few-shot Learning | cs.CV | Prevalent techniques in zero-shot learning do not generalize well to other
related problem scenarios. Here, we present a unified approach for conventional
zero-shot, generalized zero-shot and few-shot learning problems. Our approach
is based on a novel Class Adapting Principal Directions (CAPD) concept that
allows mult... | computer science |
28,498 | Fast and accurate classification of echocardiograms using deep learning | cs.CV | Echocardiography is essential to modern cardiology. However, human
interpretation limits high throughput analysis, limiting echocardiography from
reaching its full clinical and research potential for precision medicine. Deep
learning is a cutting-edge machine-learning technique that has been useful in
analyzing medical... | computer science |
28,499 | Hierarchical Model for Long-term Video Prediction | cs.CV | Video prediction has been an active topic of research in the past few years.
Many algorithms focus on pixel-level predictions, which generates results that
blur and disintegrate within a few frames. In this project, we use a
hierarchical approach for long-term video prediction. We aim at estimating
high-level structure... | computer science |
28,500 | Large-scale Datasets: Faces with Partial Occlusions and Pose Variations
in the Wild | cs.CV | Face detection methods have relied on face datasets for training. However,
existing face datasets tend to be in small scales for face learning in both
constrained and unconstrained environments. In this paper, we first introduce
our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy
Large-scale Label... | computer science |
28,501 | Independent Motion Detection with Event-driven Cameras | cs.CV | Unlike standard cameras that send intensity images at a constant frame rate,
event-driven cameras asynchronously report pixel-level brightness changes,
offering low latency and high temporal resolution (both in the order of
micro-seconds). As such, they have great potential for fast and low power
vision algorithms for ... | computer science |
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