pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Transfer Examples for Partial Domain Adaptation
Authors: Zhangjie Cao, Kaichao You, Mingsheng Long, Jian-Min Wang, Qiang Yang
Date: 2019-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Partial Domain Adaptation, Transfer Learning

Domain adaptation is critical for learning in new and unseen environments.
With domain adversarial training, deep networks can learn disentangled and
transferable features that effectively diminish the dataset shift between the
source and target domains for knowledge transfer. In the era of Big Data, the
ready availability of large-scale labeled datasets has stimulated wide interest
in partial domain adaptation (PDA), which transfers a recognizer from a labeled
large domain to an unlabeled small domain. It extends standard domain
adaptation to the scenario where target labels are only a subset of source
labels. Under the condition that target labels are unknown, the key challenge
of PDA is how to transfer relevant examples in the shared classes to promote
positive transfer, and ignore irrelevant ones in the specific classes to
mitigate negative transfer. In this work, we propose a unified approach to PDA,
Example Transfer Network (ETN), which jointly learns domain-invariant
representations across the source and target domains, and a progressive
weighting scheme that quantifies the transferability of source examples while
controlling their importance to the learning task in the target domain. A
thorough evaluation on several benchmark datasets shows that our approach
achieves state-of-the-art results for partial domain adaptation tasks.
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Authors: Nicholas Weir, David Lindenbaum, Alexei Bastidas, Adam Van Etten, Sean McPherson, Jacob Shermeyer, Varun Kumar, Hanlin Tang
Date: 2019-03-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: object-detection, Object Detection, Segmentation

Detection and segmentation of objects in overheard imagery is a challenging task. The variable density, random orientation, small size, and instance-to-instance heterogeneity of objects in overhead imagery calls for approaches distinct from existing models designed for natural scene datasets. Though new overhead imagery datasets are being developed, they almost universally comprise a single view taken from directly overhead ("at nadir"), failing to address a critical variable: look angle. By contrast, views vary in real-world overhead imagery, particularly in dynamic scenarios such as natural disasters where first looks are often over 40 degrees off-nadir. This represents an important challenge to computer vision methods, as changing view angle adds distortions, alters resolution, and changes lighting. At present, the impact of these perturbations for algorithmic detection and segmentation of objects is untested. To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of these images cover the same 665 square km geographic extent and are annotated with 126,747 building footprint labels, enabling direct assessment of the impact of viewpoint perturbation on model performance. We benchmark multiple leading segmentation and object detection models on: (1) building detection, (2) generalization to unseen viewing angles and resolutions, and (3) sensitivity of building footprint extraction to changes in resolution. We find that state of the art segmentation and object detection models struggle to identify buildings in off-nadir imagery and generalize poorly to unseen views, presenting an important benchmark to explore the broadly relevant challenge of detecting small, heterogeneous target objects in visually dynamic contexts.
Improving Object Detection with Inverted Attention
Authors: Zeyi Huang, Wei Ke, Dong Huang
Date: 2019-03-28 00:00:00
Tasks: Object, object-detection, Object Detection

Improving object detectors against occlusion, blur and noise is a critical
step to deploy detectors in real applications. Since it is not possible to
exhaust all image defects through data collection, many researchers seek to
generate hard samples in training. The generated hard samples are either images
or feature maps with coarse patches dropped out in the spatial dimensions.
Significant overheads are required in training the extra hard samples and/or
estimating drop-out patches using extra network branches. In this paper, we
improve object detectors using a highly efficient and fine-grain mechanism
called Inverted Attention (IA). Different from the original detector network
that only focuses on the dominant part of objects, the detector network with IA
iteratively inverts attention on feature maps and puts more attention on
complementary object parts, feature channels and even context. Our approach (1)
operates along both the spatial and channels dimensions of the feature maps;
(2) requires no extra training on hard samples, no extra network parameters for
attention estimation, and no testing overheads. Experiments show that our
approach consistently improved both two-stage and single-stage detectors on
benchmark databases.
Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
Authors: Adnan Siraj Rakin, Zhezhi He, Deliang Fan
Date: 2019-03-28 00:00:00
Proceeding: ICCV 2019 10

Several important security issues of Deep Neural Network (DNN) have been
raised recently associated with different applications and components. The most
widely investigated security concern of DNN is from its malicious input, a.k.a
adversarial example. Nevertheless, the security challenge of DNN's parameters
is not well explored yet. In this work, we are the first to propose a novel DNN
weight attack methodology called Bit-Flip Attack (BFA) which can crush a neural
network through maliciously flipping extremely small amount of bits within its
weight storage memory system (i.e., DRAM). The bit-flip operations could be
conducted through well-known Row-Hammer attack, while our main contribution is
to develop an algorithm to identify the most vulnerable bits of DNN weight
parameters (stored in memory as binary bits), that could maximize the accuracy
degradation with a minimum number of bit-flips. Our proposed BFA utilizes a
Progressive Bit Search (PBS) method which combines gradient ranking and
progressive search to identify the most vulnerable bit to be flipped. With the
aid of PBS, we can successfully attack a ResNet-18 fully malfunction (i.e.,
top-1 accuracy degrade from 69.8% to 0.1%) only through 13 bit-flips out of 93
million bits, while randomly flipping 100 bits merely degrades the accuracy by
less than 1%.
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
Authors: Wenbin Li, Lei Wang, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo
Date: 2019-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification

Few-shot learning in image classification aims to learn a classifier to
classify images when only few training examples are available for each class.
Recent work has achieved promising classification performance, where an
image-level feature based measure is usually used. In this paper, we argue that
a measure at such a level may not be effective enough in light of the scarcity
of examples in few-shot learning. Instead, we think a local descriptor based
image-to-class measure should be taken, inspired by its surprising success in
the heydays of local invariant features. Specifically, building upon the recent
episodic training mechanism, we propose a Deep Nearest Neighbor Neural Network
(DN4 in short) and train it in an end-to-end manner. Its key difference from
the literature is the replacement of the image-level feature based measure in
the final layer by a local descriptor based image-to-class measure. This
measure is conducted online via a $k$-nearest neighbor search over the deep
local descriptors of convolutional feature maps. The proposed DN4 not only
learns the optimal deep local descriptors for the image-to-class measure, but
also utilizes the higher efficiency of such a measure in the case of example
scarcity, thanks to the exchangeability of visual patterns across the images in
the same class. Our work leads to a simple, effective, and computationally
efficient framework for few-shot learning. Experimental study on benchmark
datasets consistently shows its superiority over the related state-of-the-art,
with the largest absolute improvement of $17\%$ over the next best. The source
code can be available from \UrlFont{https://github.com/WenbinLee/DN4.git}.
Multifaceted 4D Feature Segmentation and Extraction in Point and Field-based Datasets
Authors: Franz Sauer, Kwan-Liu Ma
Date: 2019-03-28 00:00:00
Tasks: GPU

The use of large-scale multifaceted data is common in a wide variety of
scientific applications. In many cases, this multifaceted data takes the form
of a field-based (Eulerian) and point/trajectory-based (Lagrangian)
representation as each has a unique set of advantages in characterizing a
system of study. Furthermore, studying the increasing scale and complexity of
these multifaceted datasets is limited by perceptual ability and available
computational resources, necessitating sophisticated data reduction and feature
extraction techniques. In this work, we present a new 4D feature
segmentation/extraction scheme that can operate on both the field and
point/trajectory data types simultaneously. The resulting features are
time-varying data subsets that have both a field and point-based component, and
were extracted based on underlying patterns from both data types. This enables
researchers to better explore both the spatial and temporal interplay between
the two data representations and study underlying phenomena from new
perspectives. We parallelize our approach using GPU acceleration and apply it
to real world multifaceted datasets to illustrate the types of features that
can be extracted and explored.
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
Authors: Hao Tang, Dan Xu, Nicu Sebe, Yan Yan
Date: 2019-03-28 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

The state-of-the-art approaches in Generative Adversarial Networks (GANs) are able to learn a mapping function from one image domain to another with unpaired image data. However, these methods often produce artifacts and can only be able to convert low-level information, but fail to transfer high-level semantic part of images. The reason is mainly that generators do not have the ability to detect the most discriminative semantic part of images, which thus makes the generated images with low-quality. To handle the limitation, in this paper we propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models. The attention-guided generators in AGGAN are able to produce attention masks via a built-in attention mechanism, and then fuse the input image with the attention mask to obtain a target image with high-quality. Moreover, we propose a novel attention-guided discriminator which only considers attended regions. The proposed AGGAN is trained by an end-to-end fashion with an adversarial loss, cycle-consistency loss, pixel loss and attention loss. Both qualitative and quantitative results demonstrate that our approach is effective to generate sharper and more accurate images than existing models. The code is available at https://github.com/Ha0Tang/AttentionGAN.
FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
Authors: Jingwei Huang, Yichao Zhou, Thomas Funkhouser, Leonidas Guibas
Date: 2019-03-29 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D geometry, Surface Normal Estimation

In this work, we introduce the novel problem of identifying dense canonical
3D coordinate frames from a single RGB image. We observe that each pixel in an
image corresponds to a surface in the underlying 3D geometry, where a canonical
frame can be identified as represented by three orthogonal axes, one along its
normal direction and two in its tangent plane. We propose an algorithm to
predict these axes from RGB. Our first insight is that canonical frames
computed automatically with recently introduced direction field synthesis
methods can provide training data for the task. Our second insight is that
networks designed for surface normal prediction provide better results when
trained jointly to predict canonical frames, and even better when trained to
also predict 2D projections of canonical frames. We conjecture this is because
projections of canonical tangent directions often align with local gradients in
images, and because those directions are tightly linked to 3D canonical frames
through projective geometry and orthogonality constraints. In our experiments,
we find that our method predicts 3D canonical frames that can be used in
applications ranging from surface normal estimation, feature matching, and
augmented reality.
A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network
Authors: Weiwei Zong, Joon Lee, Chang Liu, Eric Carver, Aharon Feldman, Branislava Janic, Mohamed Elshaikh, Milan Pantelic, David Hearshen, Indrin Chetty, Benjamin Movsas, Ning Wen
Date: 2019-03-29 00:00:00
Tasks: Deep Learning, General Classification

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification.
ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images
Authors: Jingbo Lin, Wei-Peng Jing, Houbing Song, Guang-Sheng Chen
Date: 2019-03-29 00:00:00
Tasks: Management, Vocal Bursts Intensity Prediction

Building footprint extraction from high-resolution aerial images is always an
essential part of urban dynamic monitoring, planning and management. It has
also been a challenging task in remote sensing research. In recent years, deep
neural networks have made great achievement in improving accuracy of building
extraction from remote sensing imagery. However, most of existing approaches
usually require large amount of parameters and floating point operations for
high accuracy, it leads to high memory consumption and low inference speed
which are harmful to research. In this paper, we proposed a novel efficient
network named ESFNet which employs separable factorized residual block and
utilizes the dilated convolutions, aiming to preserve slight accuracy loss with
low computational cost and memory consumption. Our ESFNet obtains a better
trade-off between accuracy and efficiency, it can run at over 100 FPS on single
Tesla V100, requires 6x fewer FLOPs and has 18x fewer parameters than
state-of-the-art real-time architecture ERFNet while preserving similar
accuracy without any additional context module, post-processing and pre-trained
scheme. We evaluated our networks on WHU Building Dataset and compared it with
other state-of-the-art architectures. The result and comprehensive analysis
show that our networks are benefit for efficient remote sensing researches, and
the idea can be further extended to other areas. The code is public available
at: https://github.com/mrluin/ESFNet-Pytorch
Lending Orientation to Neural Networks for Cross-view Geo-localization
Authors: Liu Liu, Hongdong Li
Date: 2019-03-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: geo-localization

This paper studies image-based geo-localization (IBL) problem using
ground-to-aerial cross-view matching. The goal is to predict the spatial
location of a ground-level query image by matching it to a large geotagged
aerial image database (e.g., satellite imagery). This is a challenging task due
to the drastic differences in their viewpoints and visual appearances. Existing
deep learning methods for this problem have been focused on maximizing feature
similarity between spatially close-by image pairs, while minimizing other
images pairs which are far apart. They do so by deep feature embedding based on
visual appearance in those ground-and-aerial images. However, in everyday life,
humans commonly use {\em orientation} information as an important cue for the
task of spatial localization. Inspired by this insight, this paper proposes a
novel method which endows deep neural networks with the `commonsense' of
orientation. Given a ground-level spherical panoramic image as query input (and
a large georeferenced satellite image database), we design a Siamese network
which explicitly encodes the orientation (i.e., spherical directions) of each
pixel of the images. Our method significantly boosts the discriminative power
of the learned deep features, leading to a much higher recall and precision
outperforming all previous methods. Our network is also more compact using only
1/5th number of parameters than a previously best-performing network. To
evaluate the generalization of our method, we also created a large-scale
cross-view localization benchmark containing 100K geotagged ground-aerial pairs
covering a city. Our codes and datasets are available at
\url{https://github.com/Liumouliu/OriCNN}.
Synthesizing a 4D Spatio-Angular Consistent Light Field from a Single Image
Authors: Andre Ivan, Williem, In Kyu Park
Date: 2019-03-29 00:00:00
Tasks: Depth Estimation

Synthesizing a densely sampled light field from a single image is highly
beneficial for many applications. The conventional method reconstructs a depth
map and relies on physical-based rendering and a secondary network to improve
the synthesized novel views. Simple pixel-based loss also limits the network by
making it rely on pixel intensity cue rather than geometric reasoning. In this
study, we show that a different geometric representation, namely, appearance
flow, can be used to synthesize a light field from a single image robustly and
directly. A single end-to-end deep neural network that does not require a
physical-based approach nor a post-processing subnetwork is proposed. Two novel
loss functions based on known light field domain knowledge are presented to
enable the network to preserve the spatio-angular consistency between
sub-aperture images effectively. Experimental results show that the proposed
model successfully synthesizes dense light fields and qualitatively and
quantitatively outperforms the previous model . The method can be generalized
to arbitrary scenes, rather than focusing on a particular class of object. The
synthesized light field can be used for various applications, such as depth
estimation and refocusing.
DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input
Authors: Zihao Bo, Hao Zhang, Junhai Yong, Feng Xu
Date: 2019-03-29 00:00:00
Tasks: Hand Segmentation, Object, Segmentation

We propose a real-time DNN-based technique to segment hand and object of
interacting motions from depth inputs. Our model is called DenseAttentionSeg,
which contains a dense attention mechanism to fuse information in different
scales and improves the results quality with skip-connections. Besides, we
introduce a contour loss in model training, which helps to generate accurate
hand and object boundaries. Finally, we propose and release our InterSegHands
dataset, a fine-scale hand segmentation dataset containing about 52k depth maps
of hand-object interactions. Our experiments evaluate the effectiveness of our
techniques and datasets, and indicate that our method outperforms the current
state-of-the-art deep segmentation methods on interaction segmentation.
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification
Authors: Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, Ling Shao
Date: 2019-03-29 00:00:00
Tasks: Person Re-Identification, Time Series, Time Series Analysis, Video-Based Person Re-Identification

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method.
Shape Robust Text Detection with Progressive Scale Expansion Network
Authors: Wenhai Wang, Enze Xie, Xiang Li, Wenbo Hou, Tong Lu, Gang Yu, Shuai Shao
Date: 2019-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Optical Character Recognition (OCR), Scene Text Detection, Text Detection

Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future.