pwc-archive/papers-with-abstracts huggingface dataset:
What I See Is What You See: Joint Attention Learning for First and Third Person Video Co-analysis
Authors: Huangyue Yu, Minjie Cai, Yunfei Liu, Feng Lu
Date: 2019-04-16 00:00:00
Tasks: Self-Supervised Learning, Triplet

In recent years, more and more videos are captured from the first-person
viewpoint by wearable cameras. Such first-person video provides additional
information besides the traditional third-person video, and thus has a wide
range of applications. However, techniques for analyzing the first-person video
can be fundamentally different from those for the third-person video, and it is
even more difficult to explore the shared information from both viewpoints. In
this paper, we propose a novel method for first- and third-person video
co-analysis. At the core of our method is the notion of "joint attention",
indicating the learnable representation that corresponds to the shared
attention regions in different viewpoints and thus links the two viewpoints. To
this end, we develop a multi-branch deep network with a triplet loss to extract
the joint attention from the first- and third-person videos via self-supervised
learning. We evaluate our method on the public dataset with cross-viewpoint
video matching tasks. Our method outperforms the state-of-the-art both
qualitatively and quantitatively. We also demonstrate how the learned joint
attention can benefit various applications through a set of additional
experiments.
Positional Encoding to Control Output Sequence Length
Authors: Sho Takase, Naoaki Okazaki
Date: 2019-04-16 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abstractive Text Summarization, Decoder, Sentence Summarization, Text Generation, Text Summarization

Neural encoder-decoder models have been successful in natural language
generation tasks. However, real applications of abstractive summarization must
consider additional constraint that a generated summary should not exceed a
desired length. In this paper, we propose a simple but effective extension of a
sinusoidal positional encoding (Vaswani et al., 2017) to enable neural
encoder-decoder model to preserves the length constraint. Unlike in previous
studies where that learn embeddings representing each length, the proposed
method can generate a text of any length even if the target length is not
present in training data. The experimental results show that the proposed
method can not only control the generation length but also improve the ROUGE
scores.
Complex Trainable ISTA for Linear and Nonlinear Inverse Problems
Authors: Satoshi Takabe, Tadashi Wadayama, Yonina C. Eldar
Date: 2019-04-16 00:00:00

Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications. In this paper, we propose a trainable iterative signal recovery algorithm named complex-field TISTA (C-TISTA) which treats complex-field nonlinear inverse problems. C-TISTA is based on the concept of deep unfolding and consists of a gradient descent step with the Wirtinger derivatives followed by a shrinkage step with a trainable complex-valued shrinkage function. Importantly, it contains a small number of trainable parameters so that its training process can be executed efficiently. Numerical results indicate that C-TISTA shows remarkable signal recovery performance compared with existing algorithms.
swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor
Authors: Mingzhen Li, Changxi Liu, Jianjin Liao, Xuegui Zheng, Hailong Yang, Rujun Sun, Jun Xu, Lin Gan, Guangwen Yang, Zhongzhi Luan, Depei Qian
Date: 2019-04-16 00:00:00
Tasks: Code Generation, Deep Learning

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79x on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor.
Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter
Authors: Quanquan Shao, Jie Hu, Weiming Wang, Yi Fang, Wenhai Liu, Jin Qi, Jin Ma
Date: 2019-04-16 00:00:00
Tasks: Diversity, Pose Estimation, Self-Supervised Learning

This paper focuses on robotic picking tasks in cluttered scenario. Because of
the diversity of poses, types of stack and complicated background in bin
picking situation, it is much difficult to recognize and estimate their pose
before grasping them. Here, this paper combines Resnet with U-net structure, a
special framework of Convolution Neural Networks (CNN), to predict picking
region without recognition and pose estimation. And it makes robotic picking
system learn picking skills from scratch. At the same time, we train the
network end to end with online samples. In the end of this paper, several
experiments are conducted to demonstrate the performance of our methods.
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
Authors: Xinyao Wang, Liefeng Bo, Li Fuxin
Date: 2019-04-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Alignment, regression, Robust Face Alignment

Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability penalizes loss more on foreground pixels while less on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks. Code will be made publicly available at https://github.com/protossw512/AdaptiveWingLoss.
Real Image Denoising with Feature Attention
Authors: Saeed Anwar, Nick Barnes
Date: 2019-04-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Color Image Denoising, Denoising, Image Denoising

Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.
Combining RGB and Points to Predict Grasping Region for Robotic Bin-Picking
Authors: Quanquan Shao, Jie Hu
Date: 2019-04-16 00:00:00
Tasks: Diversity, Pose Estimation

This paper focuses on a robotic picking tasks in cluttered scenario. Because
of the diversity of objects and clutter by placing, it is much difficult to
recognize and estimate their pose before grasping. Here, we use U-net, a
special Convolution Neural Networks (CNN), to combine RGB images and depth
information to predict picking region without recognition and pose estimation.
The efficiency of diverse visual input of the network were compared, including
RGB, RGB-D and RGB-Points. And we found the RGB-Points input could get a
precision of 95.74%.
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Authors: Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, Quoc V. Le
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Neural Architecture Search, Object, object-detection, Object Detection, Real-Time Object Detection

Current state-of-the-art convolutional architectures for object detection are
manually designed. Here we aim to learn a better architecture of feature
pyramid network for object detection. We adopt Neural Architecture Search and
discover a new feature pyramid architecture in a novel scalable search space
covering all cross-scale connections. The discovered architecture, named
NAS-FPN, consists of a combination of top-down and bottom-up connections to
fuse features across scales. NAS-FPN, combined with various backbone models in
the RetinaNet framework, achieves better accuracy and latency tradeoff compared
to state-of-the-art object detection models. NAS-FPN improves mobile detection
accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in
[32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy
with less computation time.
Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts
Authors: Rajarshi Bhowmik, Gerard de Melo
Date: 2019-04-16 00:00:00
Tasks: Decoder, Entity Disambiguation, Knowledge Graphs

Despite being vast repositories of factual information, cross-domain
knowledge graphs, such as Wikidata and the Google Knowledge Graph, only
sparsely provide short synoptic descriptions for entities. Such descriptions
that briefly identify the most discernible features of an entity provide
readers with a near-instantaneous understanding of what kind of entity they are
being presented. They can also aid in tasks such as named entity
disambiguation, ontological type determination, and answering entity queries.
Given the rapidly increasing numbers of entities in knowledge graphs, a fully
automated synthesis of succinct textual descriptions from underlying factual
information is essential. To this end, we propose a novel fact-to-sequence
encoder-decoder model with a suitable copy mechanism to generate concise and
precise textual descriptions of entities. In an in-depth evaluation, we
demonstrate that our method significantly outperforms state-of-the-art
alternatives.
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
Authors: Po-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, B. S. Manjunath
Date: 2019-04-16 00:00:00
Tasks: Denoising, feature selection

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. In addition, the proposed StackNet also achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
Authors: Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans
Date: 2019-04-16 00:00:00
Tasks: Domain Adaptation, Speaker Recognition

The I4U consortium was established to facilitate a joint entry to NIST
speaker recognition evaluations (SRE). The latest edition of such joint
submission was in SRE 2018, in which the I4U submission was among the
best-performing systems. SRE'18 also marks the 10-year anniversary of I4U
consortium into NIST SRE series of evaluation. The primary objective of the
current paper is to summarize the results and lessons learned based on the
twelve sub-systems and their fusion submitted to SRE'18. It is also our
intention to present a shared view on the advancements, progresses, and major
paradigm shifts that we have witnessed as an SRE participant in the past decade
from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm
shift from supervector representation to deep speaker embedding, and a switch
of research challenge from channel compensation to domain adaptation.
A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks
Authors: Alireza Shamsoshoara, Mehrdad Khaledi, Fatemeh Afghah, Abolfazl Razi, Jonathan Ashdown, Kurt Turck
Date: 2019-04-16 00:00:00
Tasks: Management, Reinforcement Learning

In this paper, we study the problem of spectrum scarcity in a network of
unmanned aerial vehicles (UAVs) during mission-critical applications such as
disaster monitoring and public safety missions, where the pre-allocated
spectrum is not sufficient to offer a high data transmission rate for real-time
video-streaming. In such scenarios, the UAV network can lease part of the
spectrum of a terrestrial licensed network in exchange for providing relaying
service. In order to optimize the performance of the UAV network and prolong
its lifetime, some of the UAVs will function as a relay for the primary network
while the rest of the UAVs carry out their sensing tasks. Here, we propose a
team reinforcement learning algorithm performed by the UAV's controller unit to
determine the optimum allocation of sensing and relaying tasks among the UAVs
as well as their relocation strategy at each time. We analyze the convergence
of our algorithm and present simulation results to evaluate the system
throughput in different scenarios.
Helping IT and OT Defenders Collaborate
Authors: Glenn A. Fink, Penny McKenzie
Date: 2019-04-16 00:00:00

Cyber-physical systems, especially in critical infrastructures, have become
primary hacking targets in international conflicts and diplomacy. However,
cyber-physical systems present unique challenges to defenders, starting with an
inability to communicate. This paper outlines the results of our interviews
with information technology (IT) defenders and operational technology (OT)
operators and seeks to address lessons learned from them in the structure of
our notional solutions. We present two problems in this paper: (1) the
difficulty of coordinating detection and response between defenders who work on
the cyber/IT and physical/OT sides of cyber-physical infrastructures, and (2)
the difficulty of estimating the safety state of a cyber-physical system while
an intrusion is underway but before damage can be effected by the attacker. To
meet these challenges, we propose two solutions: (1) a visualization that will
enable communication between IT defenders and OT operators, and (2) a
machine-learning approach that will estimate the distance from normal the
physical system is operating and send information to the visualization.
Something's Brewing! Early Prediction of Controversy-causing Posts from Discussion Features
Authors: Jack Hessel, Lillian Lee
Date: 2019-04-15 00:00:00
Proceeding: NAACL 2019 6

Controversial posts are those that split the preferences of a community,
receiving both significant positive and significant negative feedback. Our
inclusion of the word "community" here is deliberate: what is controversial to
some audiences may not be so to others. Using data from several different
communities on reddit.com, we predict the ultimate controversiality of posts,
leveraging features drawn from both the textual content and the tree structure
of the early comments that initiate the discussion. We find that even when only
a handful of comments are available, e.g., the first 5 comments made within 15
minutes of the original post, discussion features often add predictive capacity
to strong content-and-rate only baselines. Additional experiments on domain
transfer suggest that conversation-structure features often generalize to other
communities better than conversation-content features do.
Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction
Authors: Alesia Chernikova, Alina Oprea, Cristina Nita-Rotaru, BaekGyu Kim
Date: 2019-04-15 00:00:00
Tasks: General Classification, regression, Self-Driving Cars

Deep Neural Networks (DNNs) have tremendous potential in advancing the vision
for self-driving cars. However, the security of DNN models in this context
leads to major safety implications and needs to be better understood. We
consider the case study of steering angle prediction from camera images, using
the dataset from the 2014 Udacity challenge. We demonstrate for the first time
adversarial testing-time attacks for this application for both classification
and regression settings. We show that minor modifications to the camera image
(an L2 distance of 0.82 for one of the considered models) result in
mis-classification of an image to any class of attacker's choice. Furthermore,
our regression attack results in a significant increase in Mean Square Error
(MSE) by a factor of 69 in the worst case.
Efficiently Exploring Ordering Problems through Conflict-directed Search
Authors: Jingkai Chen, Cheng Fang, David Wang, Andrew Wang, Brian Williams
Date: 2019-04-15 00:00:00
Tasks: Benchmarking, Scheduling

In planning and scheduling, solving problems with both state and temporal
constraints is hard since these constraints may be highly coupled. Judicious
orderings of events enable solvers to efficiently make decisions over sequences
of actions to satisfy complex hybrid specifications. The ordering problem is
thus fundamental to planning. Promising recent works have explored the ordering
problem as search, incorporating a special tree structure for efficiency.
However, such approaches only reason over partial order specifications. Having
observed that an ordering is inconsistent with respect to underlying
constraints, prior works do not exploit the tree structure to efficiently
generate orderings that resolve the inconsistency. In this paper, we present
Conflict-directed Incremental Total Ordering (CDITO), a conflict-directed
search method to incrementally and systematically generate event total orders
given ordering relations and conflicts returned by sub-solvers. Due to its
ability to reason over conflicts, CDITO is much more efficient than Incremental
Total Ordering. We demonstrate this by benchmarking on temporal network
configuration problems that involve routing network flows and allocating
bandwidth resources over time.
Custom Video-Oculography Device and Its Application to Fourth Purkinje Image Detection during Saccades
Authors: Evgeniy Abdulin, Lee Friedman, Oleg Komogortsev
Date: 2019-04-15 00:00:00
Tasks: Position

We built a custom video-based eye-tracker that saves every video frame as a
full resolution image (MJPEG). Images can be processed offline for the
detection of ocular features, including the pupil and corneal reflection (First
Purkinje Image, P1) position. A comparison of multiple algorithms for detection
of pupil and corneal reflection can be performed. The system provides for
highly flexible stimulus creation, with mixing of graphic, image, and video
stimuli. We can change cameras and infrared illuminators depending on the image
qualities and frame rate desired. Using this system, we have detected the
position of the Fourth Purkinje image (P4) in the frames. We show that when we
estimate gaze by calculating P1-P4, signal compares well with gaze estimated
with a DPI eye-tracker, which natively detects and tracks the P1 and P4.
DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services
Authors: Boyang Li, Changhao Chenli, Xiaowei Xu, Yiyu Shi, Taeho Jung
Date: 2019-04-15 00:00:00
Tasks: Deep Learning, Semantic Segmentation

With the increasing artificial intelligence application, deep neural network (DNN) has become an emerging task. However, to train a good deep learning model will suffer from enormous computation cost and energy consumption. Recently, blockchain has been widely used, and during its operation, a huge amount of computation resources are wasted for the Proof of Work (PoW) consensus. In this paper, we propose DLBC to exploit the computation power of miners for deep learning training as proof of useful work instead of calculating hash values. it distinguishes itself from recent proof of useful work mechanisms by addressing various limitations of them. Specifically, DLBC handles multiple tasks, larger model and training datasets, and introduces a comprehensive ranking mechanism that considers tasks difficulty(e.g., model complexity, network burden, data size, queue length). We also applied DNN-watermark [1] to improve the robustness. In Section V, the average overhead of digital signature is 1.25, 0.001, 0.002 and 0.98 seconds, respectively, and the average overhead of network is 3.77, 3.01, 0.37 and 0.41 seconds, respectively. Embedding a watermark takes 3 epochs and removing a watermark takes 30 epochs. This penalty of removing watermark will prevent attackers from stealing, improving, and resubmitting DL models from honest miners.
Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation
Authors: Markus Wulfmeier
Date: 2019-04-15 00:00:00
Tasks: BIG-bench Machine Learning, Domain Adaptation, Imitation Learning

Recent successes in machine learning have led to a shift in the design of
autonomous systems, improving performance on existing tasks and rendering new
applications possible. Data-focused approaches gain relevance across diverse,
intricate applications when developing data collection and curation pipelines
becomes more effective than manual behaviour design. The following work aims at
increasing the efficiency of this pipeline in two principal ways: by utilising
more powerful sources of informative data and by extracting additional
information from existing data. In particular, we target three orthogonal
fronts: imitation learning, domain adaptation, and transfer from simulation.