pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Bi-directional Flow-based Video Generation from one Snapshot
Authors: Lu Sheng, Junting Pan, Jiaming Guo, Jing Shao, Xiaogang Wang, Chen Change Loy
Date: 2019-03-03 00:00:00
Tasks: Video Generation

Imagining multiple consecutive frames given one single snapshot is
challenging, since it is difficult to simultaneously predict diverse motions
from a single image and faithfully generate novel frames without visual
distortions. In this work, we leverage an unsupervised variational model to
learn rich motion patterns in the form of long-term bi-directional flow fields,
and apply the predicted flows to generate high-quality video sequences. In
contrast to the state-of-the-art approach, our method does not require external
flow supervisions for learning. This is achieved through a novel module that
performs bi-directional flows prediction from a single image. In addition, with
the bi-directional flow consistency check, our method can handle occlusion and
warping artifacts in a principled manner. Our method can be trained end-to-end
based on arbitrarily sampled natural video clips, and it is able to capture
multi-modal motion uncertainty and synthesizes photo-realistic novel sequences.
Quantitative and qualitative evaluations over synthetic and real-world datasets
demonstrate the effectiveness of the proposed approach over the
state-of-the-art methods.
Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry
Authors: Dingfu Zhou, Yuchao Dai, Hongdong Li
Date: 2019-03-03 00:00:00
Tasks: Monocular Visual Odometry, Visual Odometry

Recovering the absolute metric scale from a monocular camera is a challenging
but highly desirable problem for monocular camera-based systems. By using
different kinds of cues, various approaches have been proposed for scale
estimation, such as camera height, object size etc. In this paper, firstly, we
summarize different kinds of scale estimation approaches. Then, we propose a
robust divide and conquer the absolute scale estimation method based on the
ground plane and camera height by analyzing the advantages and disadvantages of
different approaches. By using the estimated scale, an effective scale
correction strategy has been proposed to reduce the scale drift during the
Monocular Visual Odometry (VO) estimation process. Finally, the effectiveness
and robustness of the proposed method have been verified on both public and
self-collected image sequences.
Understanding Feature Selection and Feature Memorization in Recurrent Neural Networks
Authors: Bokang Zhu, Richong Zhang, Dingkun Long, Yongyi Mao
Date: 2019-03-03 00:00:00
Tasks: feature selection, Memorization

In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study
the intrinsic capability of recurrent neural networks in sequence learning.
Four different recurrent network models are studied both analytically and
experimentally using this test. Our results suggest that in general there
exists a conflict between feature selection and feature memorization in
sequence learning. Such a conflict can be resolved either using a gating
mechanism as in LSTM, or by increasing the state dimension as in Vanilla RNN.
Gated models resolve this conflict by adaptively adjusting their state-update
equations, whereas Vanilla RNN resolves this conflict by assigning different
dimensions different tasks. Insights into feature selection and memorization in
recurrent networks are given.
MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
Authors: Anirudha Vishvakarma
Date: 2019-03-03 00:00:00
Tasks: Fine-Grained Visual Recognition, Image Retrieval, Product Recommendation, Recommendation Systems, Retrieval, Triplet

Multi-scale deep CNN architecture [1, 2, 3] successfully captures both fine
and coarse level image descriptors for visual similarity task, but they come up
with expensive memory overhead and latency. In this paper, we propose a
competing novel CNN architecture, called MILDNet, which merits by being vastly
compact (about 3 times). Inspired by the fact that successive CNN layers
represent the image with increasing levels of abstraction, we compressed our
deep ranking model to a single CNN by coupling activations from multiple
intermediate layers along with the last layer. Trained on the famous
Street2shop dataset [4], we demonstrate that our approach performs as good as
the current state-of-the-art models with only one third of the parameters,
model size, training time and significant reduction in inference time. The
significance of intermediate layers on image retrieval task has also been shown
to be performing on popular datasets Holidays, Oxford, Paris [5]. So even
though our experiments are done on ecommerce domain, it is applicable to other
domains as well. We further did an ablation study to validate our hypothesis by
checking the impact on adding each intermediate layer. With this we also
present two more useful variants of MILDNet, a mobile model (12 times smaller)
for on-edge devices and a compactly featured model (512-d feature embeddings)
for systems with less RAMs and to reduce the ranking cost. Further we present
an intuitive way to automatically create a tailored in-house triplet training
dataset, which is very hard to create manually. This solution too can also be
deployed as an all-inclusive visual similarity solution. Finally, we present
our entire production level architecture which currently powers visual
similarity at Fynd.
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection
Authors: Xuhong Wang, Ying Du, Shi-Jie Lin, Ping Cui, Yuntian Shen, Yupu Yang
Date: 2019-03-03 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Recently, deep generative models have become increasingly popular in unsupervised anomaly detection. However, deep generative models aim at recovering the data distribution rather than detecting anomalies. Besides, deep generative models have the risk of overfitting training samples, which has disastrous effects on anomaly detection performance. To solve the above two problems, we propose a Self-adversarial Variational Autoencoder with a Gaussian anomaly prior assumption. We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space. Therefore, a Gaussian transformer net T is trained to synthesize anomalous but near-normal latent variables. Keeping the original training objective of Variational Autoencoder, besides, the generator G tries to distinguish between the normal latent variables and the anomalous ones synthesized by T, and the encoder E is trained to discriminate whether the output of G is real. These new objectives we added not only give both G and E the ability to discriminate but also introduce additional regularization to prevent overfitting. Compared with the SOTA baselines, the proposed model achieves significant improvements in extensive experiments. Datasets and our model are available at a Github repository.
Competitive Bridge Bidding with Deep Neural Networks
Authors: Jiang Rong, Tao Qin, Bo An
Date: 2019-03-03 00:00:00

The game of bridge consists of two stages: bidding and playing. While playing
is proved to be relatively easy for computer programs, bidding is very
challenging. During the bidding stage, each player knowing only his/her own
cards needs to exchange information with his/her partner and interfere with
opponents at the same time. Existing methods for solving perfect-information
games cannot be directly applied to bidding. Most bridge programs are based on
human-designed rules, which, however, cannot cover all situations and are
usually ambiguous and even conflicting with each other. In this paper, we, for
the first time, propose a competitive bidding system based on deep learning
techniques, which exhibits two novelties. First, we design a compact
representation to encode the private and public information available to a
player for bidding. Second, based on the analysis of the impact of other
players' unknown cards on one's final rewards, we design two neural networks to
deal with imperfect information, the first one inferring the cards of the
partner and the second one taking the outputs of the first one as part of its
input to select a bid. Experimental results show that our bidding system
outperforms the top rule-based program.
CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling
Authors: Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang
Date: 2019-03-03 00:00:00
Tasks: Code Completion, Code Generation, Language Modelling

Recently deep learning based Natural Language Processing (NLP) models have shown great potential in the modeling of source code. However, a major limitation of these approaches is that they take source code as simple tokens of text and ignore its contextual, syntactical and structural dependencies. In this work, we present CodeGRU, a gated recurrent unit based source code language model that is capable of capturing source code's contextual, syntactical and structural dependencies. We introduce a novel approach which can capture the source code context by leveraging the source code token types. Further, we adopt a novel approach which can learn variable size context by taking into account source code's syntax, and structural information. We evaluate CodeGRU with real-world data set and it shows that CodeGRU outperforms the state-of-the-art language models and help reduce the vocabulary size up to 24.93\%. Unlike previous works, we tested CodeGRU with an independent test set which suggests that our methodology does not requisite the source code comes from the same domain as training data while providing suggestions. We further evaluate CodeGRU with two software engineering applications: source code suggestion, and source code completion. Our experiment confirms that the source code's contextual information can be vital and can help improve the software language models. The extensive evaluation of CodeGRU shows that it outperforms the state-of-the-art models. The results further suggest that the proposed approach can help reduce the vocabulary size and is of practical use for software developers.
3D convolutional neural network for abdominal aortic aneurysm segmentation
Authors: Karen López-Linares, Inmaculada García, Ainhoa García-Familiar, Iván Macía, Miguel A. González Ballester
Date: 2019-03-03 00:00:00
Tasks: Prognosis

An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if
not treated, tends to grow and may rupture. A significant unmet need in the
assessment of AAA disease, for the diagnosis, prognosis and follow-up, is the
determination of rupture risk, which is currently based on the manual
measurement of the aneurysm diameter in a selected Computed Tomography
Angiography (CTA) scan. However, there is a lack of standardization determining
the degree and rate of disease progression, due to the lack of robust,
automated aneurysm segmentation tools that allow quantitatively analyzing the
AAA. In this work, we aim at proposing the first 3D convolutional neural
network for the segmentation of aneurysms both from preoperative and
postoperative CTA scans. We extensively validate its performance in terms of
diameter measurements, to test its applicability in the clinical practice, as
well as regarding the relative volume difference, and Dice and Jaccard scores.
The proposed method yields a mean diameter measurement error of 3.3 mm, a
relative volume difference of 8.58 %, and Dice and Jaccard scores of 87 % and
77 %, respectively. At a clinical level, an aneurysm enlargement of 10 mm is
considered relevant, thus, our method is suitable to automatically determine
the AAA diameter and opens up the opportunity for more complex aneurysm
analysis.
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
Authors: Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun
Date: 2019-03-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Super-Resolution, Super-Resolution

Recent research on super-resolution has achieved great success due to the
development of deep convolutional neural networks (DCNNs). However,
super-resolution of arbitrary scale factor has been ignored for a long time.
Most previous researchers regard super-resolution of different scale factors as
independent tasks. They train a specific model for each scale factor which is
inefficient in computing, and prior work only take the super-resolution of
several integer scale factors into consideration. In this work, we propose a
novel method called Meta-SR to firstly solve super-resolution of arbitrary
scale factor (including non-integer scale factors) with a single model. In our
Meta-SR, the Meta-Upscale Module is proposed to replace the traditional upscale
module. For arbitrary scale factor, the Meta-Upscale Module dynamically
predicts the weights of the upscale filters by taking the scale factor as input
and use these weights to generate the HR image of arbitrary size. For any
low-resolution image, our Meta-SR can continuously zoom in it with arbitrary
scale factor by only using a single model. We evaluated the proposed method
through extensive experiments on widely used benchmark datasets on single image
super-resolution. The experimental results show the superiority of our
Meta-Upscale.
Face Image Reflection Removal
Authors: Renjie Wan, Boxin Shi, Haoliang Li, Ling-Yu Duan, Alex C. Kot
Date: 2019-03-03 00:00:00
Tasks: Face Recognition, Reflection Removal

Face images captured through the glass are usually contaminated by
reflections. The non-transmitted reflections make the reflection removal more
challenging than for general scenes, because important facial features are
completely occluded. In this paper, we propose and solve the face image
reflection removal problem. We remove non-transmitted reflections by
incorporating inpainting ideas into a guided reflection removal framework and
recover facial features by considering various face-specific priors. We use a
newly collected face reflection image dataset to train our model and compare
with state-of-the-art methods. The proposed method shows advantages in
estimating reflection-free face images for improving face recognition.
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference
Authors: Kelvin Hsu, Fabio Ramos
Date: 2019-03-03 00:00:00

In likelihood-free settings where likelihood evaluations are intractable,
approximate Bayesian computation (ABC) addresses the formidable inference task
to discover plausible parameters of simulation programs that explain the
observations. However, they demand large quantities of simulation calls.
Critically, hyperparameters that determine measures of simulation discrepancy
crucially balance inference accuracy and sample efficiency, yet are difficult
to tune. In this paper, we present kernel embedding likelihood-free inference
(KELFI), a holistic framework that automatically learns model hyperparameters
to improve inference accuracy given limited simulation budget. By leveraging
likelihood smoothness with conditional mean embeddings, we nonparametrically
approximate likelihoods and posteriors as surrogate densities and sample from
closed-form posterior mean embeddings, whose hyperparameters are learned under
its approximate marginal likelihood. Our modular framework demonstrates
improved accuracy and efficiency on challenging inference problems in ecology.
Recognition of Multiple Food Items in a Single Photo for Use in a Buffet-Style Restaurant
Authors: Masashi Anzawa, Sosuke Amano, Yoko Yamakata, Keiko Motonaga, Akiko Kamei, Kiyoharu Aizawa
Date: 2019-03-03 00:00:00

We investigate image recognition of multiple food items in a single photo,
focusing on a buffet restaurant application, where menu changes at every meal,
and only a few images per class are available. After detecting food areas, we
perform hierarchical recognition. We evaluate our results, comparing to two
baseline methods.
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
Authors: Gongjie Zhang, Shijian Lu, Wei zhang
Date: 2019-03-03 00:00:00
Tasks: Novel Object Detection, Object, object-detection, Object Detection

Accurate and robust detection of multi-class objects in optical remote
sensing images is essential to many real-world applications such as urban
planning, traffic control, searching and rescuing, etc. However,
state-of-the-art object detection techniques designed for images captured using
ground-level sensors usually experience a sharp performance drop when directly
applied to remote sensing images, largely due to the object appearance
differences in remote sensing images in term of sparse texture, low contrast,
arbitrary orientations, large scale variations, etc. This paper presents a
novel object detection network (CAD-Net) that exploits attention-modulated
features as well as global and local contexts to address the new challenges in
detecting objects from remote sensing images. The proposed CAD-Net learns
global and local contexts of objects by capturing their correlations with the
global scene (at scene-level) and the local neighboring objects or features (at
object-level), respectively. In addition, it designs a spatial-and-scale-aware
attention module that guides the network to focus on more informative regions
and features as well as more appropriate feature scales. Experiments over two
publicly available object detection datasets for remote sensing images
demonstrate that the proposed CAD-Net achieves superior detection performance.
The implementation codes will be made publicly available for facilitating
future researches.
Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network
Authors: Wenchao Ding, Jing Chen, Shaojie Shen
Date: 2019-03-03 00:00:00
Tasks: Autonomous Vehicles

Anticipating possible behaviors of traffic participants is an essential capability of autonomous vehicles. Many behavior detection and maneuver recognition methods only have a very limited prediction horizon that leaves inadequate time and space for planning. To avoid unsatisfactory reactive decisions, it is essential to count long-term future rewards in planning, which requires extending the prediction horizon. In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern. We adopt a recurrent neural network (RNN) for observation encoding, and based on that, we propose a novel vehicle behavior interaction network (VBIN) to capture the vehicle interaction from the hidden states and connection feature of each interaction pair. The output of our method is a probabilistic likelihood of multiple behavior classes, which matches the multimodal and uncertain nature of the distant future. A systematic comparison of our method against two state-of-the-art methods and another two baseline methods on a publicly available real highway dataset is provided, showing that our method has superior accuracy and advanced capability for interaction modeling.
Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning
Authors: Wenchao Ding, Shaojie Shen
Date: 2019-03-03 00:00:00
Tasks: Autonomous Driving, Trajectory Prediction

In this paper, we present an online two-level vehicle trajectory prediction
framework for urban autonomous driving where there are complex contextual
factors, such as lane geometries, road constructions, traffic regulations and
moving agents. Our method combines high-level policy anticipation with
low-level context reasoning. We leverage a long short-term memory (LSTM)
network to anticipate the vehicle's driving policy (e.g., forward, yield, turn
left, turn right, etc.) using its sequential history observations. The policy
is then used to guide a low-level optimization-based context reasoning process.
We show that it is essential to incorporate the prior policy anticipation due
to the multimodal nature of the future trajectory. Moreover, contrary to
existing regression-based trajectory prediction methods, our optimization-based
reasoning process can cope with complex contextual factors. The final output of
the two-level reasoning process is a continuous trajectory that automatically
adapts to different traffic configurations and accurately predicts future
vehicle motions. The performance of the proposed framework is analyzed and
validated in an emerging autonomous driving simulation platform (CARLA).
Multiple Learning for Regression in big data
Authors: Xiang Liu, Ziyang Tang, Huyunting Huang, Tonglin Zhang, Baijian Yang
Date: 2019-03-03 00:00:00
Tasks: Form, regression

Regression problems that have closed-form solutions are well understood and can be easily implemented when the dataset is small enough to be all loaded into the RAM. Challenges arise when data is too big to be stored in RAM to compute the closed form solutions. Many techniques were proposed to overcome or alleviate the memory barrier problem but the solutions are often local optimal. In addition, most approaches require accessing the raw data again when updating the models. Parallel computing clusters are also expected if multiple models need to be computed simultaneously. We propose multiple learning approaches that utilize an array of sufficient statistics (SS) to address this big data challenge. This memory oblivious approach breaks the memory barrier when computing regressions with closed-form solutions, including but not limited to linear regression, weighted linear regression, linear regression with Box-Cox transformation (Box-Cox regression) and ridge regression models. The computation and update of the SS array can be handled at per row level or per mini-batch level. And updating a model is as easy as matrix addition and subtraction. Furthermore, multiple SS arrays for different models can be easily computed simultaneously to obtain multiple models at one pass through the dataset. We implemented our approaches on Spark and evaluated over the simulated datasets. Results showed our approaches can achieve closed-form solutions of multiple models at the cost of half training time of the traditional methods for a single model.
Variational Auto-Decoder: A Method for Neural Generative Modeling from Incomplete Data
Authors: Amir Zadeh, Yao-Chong Lim, Paul Pu Liang, Louis-Philippe Morency
Date: 2019-03-03 00:00:00
Tasks: Decoder, Missing Values

Learning a generative model from partial data (data with missingness) is a challenging area of machine learning research. We study a specific implementation of the Auto-Encoding Variational Bayes (AEVB) algorithm, named in this paper as a Variational Auto-Decoder (VAD). VAD is a generic framework which uses Variational Bayes and Markov Chain Monte Carlo (MCMC) methods to learn a generative model from partial data. The main distinction between VAD and Variational Auto-Encoder (VAE) is the encoder component, as VAD does not have one. Using a proposed efficient inference method from a multivariate Gaussian approximate posterior, VAD models allow inference to be performed via simple gradient ascent rather than MCMC sampling from a probabilistic decoder. This technique reduces the inference computational cost, allows for using more complex optimization techniques during latent space inference (which are shown to be crucial due to a high degree of freedom in the VAD latent space), and keeps the framework simple to implement. Through extensive experiments over several datasets and different missing ratios, we show that encoders cannot efficiently marginalize the input volatility caused by imputed missing values. We study multimodal datasets in this paper, which is a particular area of impact for VAD models.