2,444 research outputs found
Adaptive Image Stream Classification via Convolutional Neural Network with Intrinsic Similarity Metrics
When performing data classification over a stream of continuously occurring
instances, a key challenge is to develop an open-world classifier that
anticipates instances from an unknown class. Studies addressing this problem,
typically called novel class detection, have considered classification methods
that reactively adapt to such changes along the stream. Importantly, they rely
on the property of cohesion and separation among instances in feature space.
Instances belonging to the same class are assumed to be closer to each other
(cohesion) than those belonging to different classes (separation).
Unfortunately, this assumption may not have large support when dealing with
high dimensional data such as images. In this paper, we address this key
challenge by proposing a semisupervised multi-task learning framework called
CSIM which aims to intrinsically search for a latent space suitable for
detecting labels of instances from both known and unknown classes.
Particularly, we utilize a convolution neural network layer that aids in the
learning of a latent feature space suitable for novel class detection. We
empirically measure the performance of CSIM over multiple realworld image
datasets and demonstrate its superiority by comparing its performance with
existing semi-supervised methods.Comment: 10 pages; KDD'18 Deep Learning Day, August 2018, London, U
Deep Fundamental Matrix Estimation without Correspondences
Estimating fundamental matrices is a classic problem in computer vision.
Traditional methods rely heavily on the correctness of estimated key-point
correspondences, which can be noisy and unreliable. As a result, it is
difficult for these methods to handle image pairs with large occlusion or
significantly different camera poses. In this paper, we propose novel neural
network architectures to estimate fundamental matrices in an end-to-end manner
without relying on point correspondences. New modules and layers are introduced
in order to preserve mathematical properties of the fundamental matrix as a
homogeneous rank-2 matrix with seven degrees of freedom. We analyze performance
of the proposed models using various metrics on the KITTI dataset, and show
that they achieve competitive performance with traditional methods without the
need for extracting correspondences.Comment: ECCV 2018, Geometry Meets Deep Learning Worksho
Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images
In this paper, we develop a complete pipeline for stain normalization,
segmentation, and classification of nuclei in hematoxylin and eosin (H&E)
stained breast cancer histopathology images. In the first step, we use a
CNN-based stain transfer technique to normalize the staining characteristics of
(H&E) images. We then train a neural network to segment images of nuclei from
the H&E images. Finally, we train an Information Maximizing Generative
Adversarial Network (InfoGAN) to learn visual representations of different
types of nuclei and classify them in an entirely unsupervised manner. The
results show that our proposed CNN stain normalization yields improved visual
similarity and cell segmentation performance compared to the conventional
SVD-based stain normalization method. In the final step of our pipeline, we
demonstrate the ability to perform fully unsupervised clustering of various
breast histopathology cell types based on morphological and color attributes.
In addition, we quantitatively evaluate our neural network - based techniques
against various quantitative metrics to validate the effectiveness of our
pipeline.Comment: 9 pages, 8 figure
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
Salient object detection (SOD), which aims to find the most important region
of interest and segment the relevant object/item in that area, is an important
yet challenging vision task. This problem is inspired by the fact that human
seems to perceive main scene elements with high priorities. Thus, accurate
detection of salient objects in complex scenes is critical for human-computer
interaction. In this paper, we present a novel feature learning framework for
SOD, in which we cast the SOD as a pixel-wise classification problem. The
proposed framework utilizes a densely hierarchical feature fusion network,
named HyperFusion-Net, automatically predicts the most important area and
segments the associated objects in an end-to-end manner. Specifically, inspired
by the human perception system and image reflection separation, we first
decompose input images into reflective image pairs by content-preserving
transforms. Then, the complementary information of reflective image pairs is
jointly extracted by an interweaved convolutional neural network (ICNN) and
hierarchically combined with a hyper-dense fusion mechanism. Based on the fused
multi-scale features, our method finally achieves a promising way of predicting
SOD. As shown in our extensive experiments, the proposed method consistently
outperforms other state-of-the-art methods on seven public datasets with a
large margin.Comment: Submmited to ECCV 2018, 16 pages, including 6 figures and 4 tables.
arXiv admin note: text overlap with arXiv:1802.0652
Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition
Deep metric learning is essential for visual recognition. The widely used
pair-wise (or triplet) based loss objectives cannot make full use of semantical
information in training samples or give enough attention to those hard samples
during optimization. Thus, they often suffer from a slow convergence rate and
inferior performance. In this paper, we show how to learn an importance-driven
distance metric via optimal transport programming from batches of samples. It
can automatically emphasize hard examples and lead to significant improvements
in convergence. We propose a new batch-wise optimal transport loss and combine
it in an end-to-end deep metric learning manner. We use it to learn the
distance metric and deep feature representation jointly for recognition.
Empirical results on visual retrieval and classification tasks with six
benchmark datasets, i.e., MNIST, CIFAR10, SHREC13, SHREC14, ModelNet10, and
ModelNet40, demonstrate the superiority of the proposed method. It can
accelerate the convergence rate significantly while achieving a
state-of-the-art recognition performance. For example, in 3D shape recognition
experiments, we show that our method can achieve better recognition performance
within only 5 epochs than what can be obtained by mainstream 3D shape
recognition approaches after 200 epochs.Comment: 10 pages, 4 figures Accepted by CVPR201
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications
Over the last few years, machine learning over graph structures has
manifested a significant enhancement in text mining applications such as event
detection, opinion mining, and news recommendation. One of the primary
challenges in this regard is structuring a graph that encodes and encompasses
the features of textual data for the effective machine learning algorithm.
Besides, exploration and exploiting of semantic relations is regarded as a
principal step in text mining applications. However, most of the traditional
text mining methods perform somewhat poor in terms of employing such relations.
In this paper, we propose a sentence-level graph-based text representation
which includes stop words to consider semantic and term relations. Then, we
employ a representation learning approach on the combined graphs of sentences
to extract the latent and continuous features of the documents. Eventually, the
learned features of the documents are fed into a deep neural network for the
sentiment classification task. The experimental results demonstrate that the
proposed method substantially outperforms the related sentiment analysis
approaches based on several benchmark datasets. Furthermore, our method can be
generalized on different datasets without any dependency on pre-trained word
embeddings.Comment: 33 pages, 6 figures, 6 Tables, Accepted for publication in Expert
Systems With Applications Journa
Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results
A new paradigm is beginning to emerge in Radiology with the advent of
increased computational capabilities and algorithms. This has led to the
ability of real time learning by computer systems of different lesion types to
help the radiologist in defining disease. For example, using a deep learning
network, we developed and tested a multiparametric deep learning (MPDL) network
for segmentation and classification using multiparametric magnetic resonance
imaging (mpMRI) radiological images. The MPDL network was constructed from
stacked sparse autoencoders with inputs from mpMRI. Evaluation of MPDL
consisted of cross-validation, sensitivity, and specificity. Dice similarity
between MPDL and post-DCE lesions were evaluated. We demonstrate high
sensitivity and specificity for differentiation of malignant from benign
lesions of 90% and 85% respectively with an AUC of 0.93. The Integrated MPDL
method accurately segmented and classified different breast tissue from
multiparametric breast MRI using deep leaning tissue signatures.Comment: Deep Learning, Machine learning, Magnetic resonance imaging,
multiparametric MRI, Breast, Cancer, Diffusion, tissue biomarker
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
Face Recognition in Low Quality Images: A Survey
Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.Comment: There are some mistakes addressing in this paper which will be
misleading to the reader and we wont have a new version in short time. We
will resubmit once it is being corecte
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