pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Image Decomposition in Vector Layers
Authors: Othman Sbai, Camille Couprie, Mathieu Aubry
Date: 2018-12-13 00:00:00
Tasks: Image Generation, Image Reconstruction, Image Retrieval, Unsupervised Image Decomposition, Vector Graphics

Deep image generation is becoming a tool to enhance artists and designers creativity potential. In this paper, we aim at making the generation process more structured and easier to interact with. Inspired by vector graphics systems, we propose a new deep image reconstruction paradigm where the outputs are composed from simple layers, defined by their color and a vector transparency mask. This presents a number of advantages compared to the commonly used convolutional network architectures. In particular, our layered decomposition allows simple user interaction, for example to update a given mask, or change the color of a selected layer. From a compact code, our architecture also generates vector images with a virtually infinite resolution, the color at each point in an image being a parametric function of its coordinates. We validate the efficiency of our approach by comparing reconstructions with state-of-the-art baselines given similar memory resources on CelebA and ImageNet datasets. Most importantly, we demonstrate several applications of our new image representation obtained in an unsupervised manner, including editing, vectorization and image search.
Human Motion Prediction via Spatio-Temporal Inpainting
Authors: Alejandro Hernandez Ruiz, Juergen Gall, Francesc Moreno-Noguer
Date: 2018-12-13 00:00:00
Tasks: Generative Adversarial Network, Human motion prediction, Motion Forecasting, motion prediction, Position, Prediction

We propose a Generative Adversarial Network (GAN) to forecast 3D human motion given a sequence of past 3D skeleton poses. While recent GANs have shown promising results, they can only forecast plausible motion over relatively short periods of time (few hundred milliseconds) and typically ignore the absolute position of the skeleton w.r.t. the camera. Our scheme provides long term predictions (two seconds or more) for both the body pose and its absolute position. Our approach builds upon three main contributions. First, we represent the data using a spatio-temporal tensor of 3D skeleton coordinates which allows formulating the prediction problem as an inpainting one, for which GANs work particularly well. Secondly, we design an architecture to learn the joint distribution of body poses and global motion, capable to hypothesize large chunks of the input 3D tensor with missing data. And finally, we argue that the L2 metric, considered so far by most approaches, fails to capture the actual distribution of long-term human motion. We propose two alternative metrics, based on the distribution of frequencies, that are able to capture more realistic motion patterns. Extensive experiments demonstrate our approach to significantly improve the state of the art, while also handling situations in which past observations are corrupted by occlusions, noise and missing frames.
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
Authors: Alessandro Di Martino, Erik Bodin, Carl Henrik Ek, Neill D. F. Campbell
Date: 2018-12-13 00:00:00

The shape of an object is an important characteristic for many vision
problems such as segmentation, detection and tracking. Being independent of
appearance, it is possible to generalize to a large range of objects from only
small amounts of data. However, shapes represented as silhouette images are
challenging to model due to complicated likelihood functions leading to
intractable posteriors. In this paper we present a generative model of shapes
which provides a low dimensional latent encoding which importantly resides on a
smooth manifold with respect to the silhouette images. The proposed model
propagates uncertainty in a principled manner allowing it to learn from small
amounts of data and providing predictions with associated uncertainty. We
provide experiments that show how our proposed model provides favorable
quantitative results compared with the state-of-the-art while simultaneously
providing a representation that resides on a low-dimensional interpretable
manifold.
Learning Features of Network Structures Using Graphlets
Authors: Kun Tu, Jian Li, Don Towsley, Dave Braines, Liam Turner
Date: 2018-12-13 00:00:00
Tasks: General Classification, Learning Network Representations, Network Embedding

Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic applications, these networks may vary over time. Modeling and analyzing such temporal properties is of additional interest as it can provide a richer characterization of relations between nodes in networks. In this paper, we explore the role of \emph{graphlets} in network classification for both static and temporal networks. Graphlets are small non-isomorphic induced subgraphs representing connected patterns in a network and their frequency can be used to assess network structures. We show that graphlet features, which are not captured by state-of-the-art methods, play a significant role in enhancing the performance of network classification. To that end, we propose two novel graphlet-based techniques, \emph{gl2vec} for network embedding, and \emph{gl-DCNN} for diffusion-convolutional neural networks. We demonstrate the efficacy and usability of \emph{gl2vec} and \emph{gl-DCNN} through extensive experiments using several real-world static and temporal networks. We find that features learned from graphlets can bring notable performance increases to state-of-the-art methods in network analysis.
Using Motion and Internal Supervision in Object Recognition
Authors: Daniel Harari
Date: 2018-12-13 00:00:00
Tasks: Motion Segmentation, Object, Object Recognition

In this thesis we address two related aspects of visual object recognition:
the use of motion information, and the use of internal supervision, to help
unsupervised learning. These two aspects are inter-related in the current
study, since image motion is used for internal supervision, via the detection
of spatiotemporal events of active-motion and the use of tracking. Most current
work in object recognition deals with static images during both learning and
recognition. In contrast, we are interested in a dynamic scene where visual
processes, such as detecting motion events and tracking, contribute
spatiotemporal information, which is useful for object attention, motion
segmentation, 3-D understanding and object interactions. We explore the use of
these sources of information in both learning and recognition processes. In the
first part of the work, we demonstrate how motion can be used for adaptive
detection of object-parts in dynamic environments, while automatically learning
new object appearances and poses. In the second and main part of the study we
develop methods for using specific types of visual motion to solve two
difficult problems in unsupervised visual learning: learning to recognize hands
by their appearance and by context, and learning to extract direction of gaze.
We use our conclusions in this part to propose a model for several aspects of
learning by human infants from their visual environment.
Generating Hard Examples for Pixel-wise Classification
Authors: Hyungtae Lee, Heesung Kwon, Wonkook Kim
Date: 2018-12-13 00:00:00
Tasks: Classification, Hyperspectral Image Classification, image-classification, Image Classification, Robust classification

Pixel-wise classification in remote sensing identifies entities in large-scale satellite-based images at the pixel level. Few fully annotated large-scale datasets for pixel-wise classification exist due to the challenges of annotating individual pixels. Training data scarcity inevitably ensues from the annotation challenge, leading to overfitting classifiers and degraded classification performance. The lack of annotated pixels also necessarily results in few hard examples of various entities critical for generating a robust classification hyperplane. To overcome the problem of the data scarcity and lack of hard examples in training, we introduce a two-step hard example generation (HEG) approach that first generates hard example candidates and then mines actual hard examples. In the first step, a generator that creates hard example candidates is learned via the adversarial learning framework by fooling a discriminator and a pixel-wise classification model at the same time. In the second step, mining is performed to build a fixed number of hard examples from a large pool of real and artificially generated examples. To evaluate the effectiveness of the proposed HEG approach, we design a 9-layer fully convolutional network suitable for pixel-wise classification. Experiments show that using generated hard examples from the proposed HEG approach improves the pixel-wise classification model's accuracy on red tide detection and hyperspectral image classification tasks.
Lenia - Biology of Artificial Life
Authors: Bert Wang-Chak Chan
Date: 2018-12-13 00:00:00
Tasks: Artificial Life, Diversity

We report a new system of artificial life called Lenia (from Latin lenis "smooth"), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule. Computer simulations show that Lenia supports a great diversity of complex autonomous patterns or "lifeforms" bearing resemblance to real-world microscopic organisms. More than 400 species in 18 families have been identified, many discovered via interactive evolutionary computation. They differ from other cellular automata patterns in being geometric, metameric, fuzzy, resilient, adaptive, and rule-generic. We present basic observations of the system regarding the properties of space-time and basic settings. We provide a broad survey of the lifeforms, categorize them into a hierarchical taxonomy, and map their distribution in the parameter hyperspace. We describe their morphological structures and behavioral dynamics, propose possible mechanisms of their self-propulsion, self-organization and plasticity. Finally, we discuss how the study of Lenia would be related to biology, artificial life, and artificial intelligence.
On the Differences between L2-Boosting and the Lasso
Authors: Michael Vogt
Date: 2018-12-13 00:00:00

We prove that L2-Boosting lacks a theoretical property which is central to
the behaviour of l1-penalized methods such as basis pursuit and the Lasso:
Whereas l1-penalized methods are guaranteed to recover the sparse parameter
vector in a high-dimensional linear model under an appropriate restricted
nullspace property, L2-Boosting is not guaranteed to do so. Hence, L2-Boosting
behaves quite differently from l1-penalized methods when it comes to parameter
recovery/estimation in high-dimensional linear models.
DLOW: Domain Flow for Adaptation and Generalization
Authors: Rui Gong, Wen Li, Yu-Hua Chen, Luc van Gool
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Semantic Segmentation, Style Generalization

In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other. The benefits of our DLOW model are two-fold. First, it is able to transfer source images into different styles in the intermediate domains. The transferred images smoothly bridge the gap between source and target domains, thus easing the domain adaptation task. Second, when multiple target domains are provided for training, our DLOW model is also able to generate new styles of images that are unseen in the training data. We implement our DLOW model based on CycleGAN. A domainness variable is introduced to guide the model to generate the desired intermediate domain images. In the inference phase, a flow of various styles of images can be obtained by varying the domainness variable. We demonstrate the effectiveness of our model for both cross-domain semantic segmentation and the style generalization tasks on benchmark datasets. Our implementation is available at https://github.com/ETHRuiGong/DLOW.
Geometrical Stem Detection from Image Data for Precision Agriculture
Authors: F. Langer, L. Mandtler, A. Milioto, E. Palazzolo, C. Stachniss
Date: 2018-12-13 00:00:00
Tasks: CPU, Position

High efficiency in precision farming depends on accurate tools to perform
weed detection and mapping of crops. This allows for precise removal of harmful
weeds with a lower amount of pesticides, as well as increase of the harvest's
yield by providing the farmer with valuable information. In this paper, we
address the problem of fully automatic stem detection from image data for this
purpose. Our approach runs on mobile agricultural robots taking RGB images.
After processing the images to obtain a vegetation mask, our approach separates
each plant into its individual leaves and later estimates a precise stem
position. This allows an upstream mapping algorithm to add the high-resolution
stem positions as a semantic aggregate to the global map of the robot, which
can be used for weeding and for analyzing crop statistics. We implemented our
approach and thoroughly tested it on three different datasets with vegetation
masks and stem position ground truth. The experiments presented in this paper
conclude that our module is able to detect leaves and estimate the stem's
position at a rate of 56 Hz on a single CPU. We furthermore provide the
software to the community.
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
Authors: Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan
Date: 2018-12-13 00:00:00
Tasks: Cloze Test, Natural Language Inference, Natural Language Understanding, Relation

Natural language understanding is a challenging problem that covers a wide
range of tasks. While previous methods generally train each task separately, we
consider combining the cross-task features to enhance the task performance. In
this paper, we incorporate the logic information with the help of the Natural
Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on
SCT considered various semantic information, such as sentiment and topic, but
lack the logic information between sentences which is an essential element of
stories. Thus we propose to extract the logic information during the course of
the story to improve the understanding of the whole story. The logic
information is modeled with the help of the NLI task. Experimental results
prove the strength of the logic information.
Abstractive Text Summarization by Incorporating Reader Comments
Authors: Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan
Date: 2018-12-13 00:00:00
Tasks: Abstractive Text Summarization, Reader-Aware Summarization, Text Summarization

In neural abstractive summarization field, conventional sequence-to-sequence
based models often suffer from summarizing the wrong aspect of the document
with respect to the main aspect. To tackle this problem, we propose the task of
reader-aware abstractive summary generation, which utilizes the reader comments
to help the model produce better summary about the main aspect. Unlike
traditional abstractive summarization task, reader-aware summarization
confronts two main challenges: (1) Comments are informal and noisy; (2) jointly
modeling the news document and the reader comments is challenging. To tackle
the above challenges, we design an adversarial learning model named
reader-aware summary generator (RASG), which consists of four components: (1) a
sequence-to-sequence based summary generator; (2) a reader attention module
capturing the reader focused aspects; (3) a supervisor modeling the semantic
gap between the generated summary and reader focused aspects; (4) a goal
tracker producing the goal for each generation step. The supervisor and the
goal tacker are used to guide the training of our framework in an adversarial
manner. Extensive experiments are conducted on our large-scale real-world text
summarization dataset, and the results show that RASG achieves the
state-of-the-art performance in terms of both automatic metrics and human
evaluations. The experimental results also demonstrate the effectiveness of
each module in our framework. We release our large-scale dataset for further
research.
Dynamic Feature Generation Network for Answer Selection
Authors: Longxuan Ma, Pengfei Wang, Lei Zhang
Date: 2018-12-13 00:00:00
Tasks: Answer Selection, Retrieval, Sentence

Extracting appropriate features to represent a corpus is an important task
for textual mining. Previous attention based work usually enhance feature at
the lexical level, which lacks the exploration of feature augmentation at the
sentence level. In this paper, we exploit a Dynamic Feature Generation Network
(DFGN) to solve this problem. Specifically, DFGN generates features based on a
variety of attention mechanisms and attaches features to sentence
representation. Then a thresholder is designed to filter the mined features
automatically. DFGN extracts the most significant characteristics from datasets
to keep its practicability and robustness. Experimental results on multiple
well-known answer selection datasets show that our proposed approach
significantly outperforms state-of-the-art baselines. We give a detailed
analysis of the experiments to illustrate why DFGN provides excellent retrieval
and interpretative ability.
Representation, Justification and Explanation in a Value Driven Agent: An Argumentation-Based Approach
Authors: Beishui Liao, Michael Anderson, Susan Leigh Anderson
Date: 2018-12-13 00:00:00
Tasks: Decision Making, Epistemic Reasoning, Explainable artificial intelligence, Philosophy

Ethical and explainable artificial intelligence is an interdisciplinary research area involving computer science, philosophy, logic, the social sciences, etc. For an ethical autonomous system, the ability to justify and explain its decision making is a crucial aspect of transparency and trustworthiness. This paper takes a Value Driven Agent (VDA) as an example, explicitly representing implicit knowledge of a machine learning-based autonomous agent and using this formalism to justify and explain the decisions of the agent. For this purpose, we introduce a novel formalism to describe the intrinsic knowledge and solutions of a VDA in each situation. Based on this formalism, we formulate an approach to justify and explain the decision-making process of a VDA, in terms of a typical argumentation formalism, Assumption-based Argumentation (ABA). As a result, a VDA in a given situation is mapped onto an argumentation framework in which arguments are defined by the notion of deduction. Justified actions with respect to semantics from argumentation correspond to solutions of the VDA. The acceptance (rejection) of arguments and their premises in the framework provides an explanation for why an action was selected (or not). Furthermore, we go beyond the existing version of VDA, considering not only practical reasoning, but also epistemic reasoning, such that the inconsistency of knowledge of the VDA can be identified, handled and explained.
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
Authors: Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Jianjun Zhao, Yang Liu
Date: 2018-12-13 00:00:00
Tasks: Deep Learning, Speech-to-Text

Deep learning (DL) defines a data-driven programming paradigm that
automatically composes the system decision logic from the training data. In
company with the data explosion and hardware acceleration during the past
decade, DL achieves tremendous success in many cutting-edge applications.
However, even the state-of-the-art DL systems still suffer from quality and
reliability issues. It was only until recently that some preliminary progress
was made in testing feed-forward DL systems. In contrast to feed-forward DL
systems, recurrent neural networks (RNN) follow a very different architectural
design, implementing temporal behaviors and memory with loops and internal
states. Such stateful nature of RNN contributes to its success in handling
sequential inputs such as audio, natural languages and video processing, but
also poses new challenges for quality assurance.
  In this paper, we initiate the very first step towards testing RNN-based
stateful DL systems. We model RNN as an abstract state transition system, based
on which we define a set of test coverage criteria specialized for stateful DL
systems. Moreover, we propose an automated testing framework, DeepCruiser,
which systematically generates tests in large scale to uncover defects of
stateful DL systems with coverage guidance. Our in-depth evaluation on a
state-of-the-art speech-to-text DL system demonstrates the effectiveness of our
technique in improving quality and reliability of stateful DL systems.
Wider Channel Attention Network for Remote Sensing Image Super-resolution
Authors: Jun Gu, Guangluan Xu, Yue Zhang, Xian Sun, Ran Wen, Lei Wang
Date: 2018-12-13 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recently, deep convolutional neural networks (CNNs) have obtained promising
results in image processing tasks including super-resolution (SR). However,
most CNN-based SR methods treat low-resolution (LR) inputs and features equally
across channels, rarely notice the loss of information flow caused by the
activation function and fail to leverage the representation ability of CNNs. In
this letter, we propose a novel single-image super-resolution (SISR) algorithm
named Wider Channel Attention Network (WCAN) for remote sensing images.
Firstly, the channel attention mechanism is used to adaptively recalibrate the
importance of each channel at the middle of the wider attention block (WAB).
Secondly, we propose the Local Memory Connection (LMC) to enhance the
information flow. Finally, the features within each WAB are fused to take
advantage of the network's representation capability and further improve
information and gradient flow. Analytic experiments on a public remote sensing
data set (UC Merced) show that our WCAN achieves better accuracy and visual
improvements against most state-of-the-art methods.
Omni-directional Feature Learning for Person Re-identification
Authors: Di Wu, Hong-Wei Yang, De-Shuang Huang
Date: 2018-12-13 00:00:00
Tasks: Person Re-Identification, Representation Learning, Triplet

Person re-identification (PReID) has received increasing attention due to it
is an important part in intelligent surveillance. Recently, many
state-of-the-art methods on PReID are part-based deep models. Most of them
focus on learning the part feature representation of person body in horizontal
direction. However, the feature representation of body in vertical direction is
usually ignored. Besides, the spatial information between these part features
and the different feature channels is not considered. In this study, we
introduce a multi-branches deep model for PReID. Specifically, the model
consists of five branches. Among the five branches, two of them learn the local
feature with spatial information from horizontal or vertical orientations,
respectively. The other one aims to learn interdependencies knowledge between
different feature channels generated by the last convolution layer. The remains
of two other branches are identification and triplet sub-networks, in which the
discriminative global feature and a corresponding measurement can be learned
simultaneously. All the five branches can improve the representation learning.
We conduct extensive comparative experiments on three PReID benchmarks
including CUHK03, Market-1501 and DukeMTMC-reID. The proposed deep framework
outperforms many state-of-the-art in most cases.