pwc-archive/papers-with-abstracts huggingface dataset:
3D-BEVIS: Bird's-Eye-View Instance Segmentation
Authors: Cathrin Elich, Francis Engelmann, Theodora Kontogianni, Bastian Leibe
Date: 2019-04-03 00:00:00
Tasks: 3D Instance Segmentation, 3D Semantic Instance Segmentation, Clustering, Deep Learning, General Classification, Instance Segmentation, Segmentation, Semantic Segmentation

Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird's-eye view representation.
Probing Biomedical Embeddings from Language Models
Authors: Qiao Jin, Bhuwan Dhingra, William W. Cohen, Xinghua Lu
Date: 2019-04-03 00:00:00
Proceeding: WS 2019 6
Tasks: Articles, NER, Word Embeddings

Contextualized word embeddings derived from pre-trained language models (LMs)
show significant improvements on downstream NLP tasks. Pre-training on
domain-specific corpora, such as biomedical articles, further improves their
performance. In this paper, we conduct probing experiments to determine what
additional information is carried intrinsically by the in-domain trained
contextualized embeddings. For this we use the pre-trained LMs as fixed feature
extractors and restrict the downstream task models to not have additional
sequence modeling layers. We compare BERT, ELMo, BioBERT and BioELMo, a
biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while
fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a
fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We
use visualization and nearest neighbor analysis to show that better encoding of
entity-type and relational information leads to this superiority.
Low-Depth Optical Neural Networks
Authors: Xiao-Ming Zhang, Man-Hong Yung
Date: 2019-04-03 00:00:00

Optical neural network (ONN) is emerging as an attractive proposal for machine-learning applications, enabling high-speed computation with low-energy consumption. However, there are several challenges in applying ONN for industrial applications, including the realization of activation functions and maintaining stability. In particular, the stability of ONNs decrease with the circuit depth, limiting the scalability of the ONNs for practical uses. Here we demonstrate how to compress the circuit depth of ONN to scale only logarithmically, leading to an exponential gain in terms of noise robustness. Our low-depth (LD) ONN is based on an architecture, called Optical CompuTing Of dot-Product UnitS (OCTOPUS), which can also be applied individually as a linear perceptron for solving classification problems. Using the standard data set of Letter Recognition, we present numerical evidence showing that LD-ONN can exhibit a significant gain in noise robustness, compared with a previous ONN proposal based on singular-value decomposition [Nature Photonics 11, 441 (2017)].
Constrained Generative Adversarial Networks for Interactive Image Generation
Authors: Eric Heim
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation

Generative Adversarial Networks (GANs) have received a great deal of
attention due in part to recent success in generating original, high-quality
samples from visual domains. However, most current methods only allow for users
to guide this image generation process through limited interactions. In this
work we develop a novel GAN framework that allows humans to be "in-the-loop" of
the image generation process. Our technique iteratively accepts relative
constraints of the form "Generate an image more like image A than image B".
After each constraint is given, the user is presented with new outputs from the
GAN, informing the next round of feedback. This feedback is used to constrain
the output of the GAN with respect to an underlying semantic space that can be
designed to model a variety of different notions of similarity (e.g. classes,
attributes, object relationships, color, etc.). In our experiments, we show
that our GAN framework is able to generate images that are of comparable
quality to equivalent unsupervised GANs while satisfying a large number of the
constraints provided by users, effectively changing a GAN into one that allows
users interactive control over image generation without sacrificing image
quality.
Determining input variable ranges in Industry 4.0: A heuristic for estimating the domain of a real-valued function or trained regression model given an output range
Authors: Noelia Oses, Aritz Legarretaetxebarria, Marco Quartulli, Igor García, Mikel Serrano
Date: 2019-04-03 00:00:00
Tasks: regression

Industrial process control systems try to keep an output variable within a
given tolerance around a target value. PID control systems have been widely
used in industry to control input variables in order to reach this goal.
However, this kind of Transfer Function based approach cannot be extended to
complex processes where input data might be non-numeric, high dimensional,
sparse, etc. In such cases, there is still a need for determining the subspace
of input data that produces an output within a given range. This paper presents
a non-stochastic heuristic to determine input values for a mathematical
function or trained regression model given an output range. The proposed method
creates a synthetic training data set of input combinations with a class label
that indicates whether the output is within the given target range or not.
Then, a decision tree classifier is used to determine the subspace of input
data of interest. This method is more general than a traditional controller as
the target range for the output does not have to be centered around a reference
value and it can be applied given a regression model of the output variable,
which may have categorical variables as inputs and may be high dimensional,
sparse... The proposed heuristic is validated with a proof of concept on a real
use case where the quality of a lamination factory is established to identify
the suitable subspace of production variable values.
Tiered Latent Representations and Latent Spaces for Molecular Graphs
Authors: Daniel T. Chang
Date: 2019-03-21 00:00:00

Molecular graphs generally contain subgraphs (known as groups) that are
identifiable and significant in composition, functionality, geometry, etc. Flat
latent representations (node embeddings or graph embeddings) fail to represent,
and support the use of, groups. Fully hierarchical latent representations, on
the other hand, are difficult to learn and, even if learned, may be too complex
to use or interpret. We propose tiered latent representations and latent spaces
for molecular graphs as a simple way to explicitly represent and utilize
groups, which consist of the atom (node) tier, the group tier and the molecule
(graph) tier. Specifically, we propose an architecture for learning tiered
latent representations and latent spaces using graph autoencoders, graph neural
networks, differentiable group pooling and the membership matrix. We discuss
its various components, major challenges and related work, for both a
deterministic and a probabilistic model. We also briefly discuss the usage and
exploration of tiered latent spaces. The tiered approach is applicable to other
types of structured graphs similar in nature to molecular graphs.
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
Authors: Chaohui Yu, Jindong Wang, Yiqiang Chen, Zijing Wu
Date: 2019-03-25 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Deep unsupervised domain adaptation (UDA) has recently received increasing
attention from researchers. However, existing methods are computationally
intensive due to the computation cost of Convolutional Neural Networks (CNN)
adopted by most work. To date, there is no effective network compression method
for accelerating these models. In this paper, we propose a unified Transfer
Channel Pruning (TCP) approach for accelerating UDA models. TCP is capable of
compressing the deep UDA model by pruning less important channels while
simultaneously learning transferable features by reducing the cross-domain
distribution divergence. Therefore, it reduces the impact of negative transfer
and maintains competitive performance on the target task. To the best of our
knowledge, TCP is the first approach that aims at accelerating deep UDA models.
TCP is validated on two benchmark datasets-Office-31 and ImageCLEF-DA with two
common backbone networks-VGG16 and ResNet50. Experimental results demonstrate
that TCP achieves comparable or better classification accuracy than other
comparison methods while significantly reducing the computational cost. To be
more specific, in VGG16, we get even higher accuracy after pruning 26% floating
point operations (FLOPs); in ResNet50, we also get higher accuracy on half of
the tasks after pruning 12% FLOPs. We hope that TCP will open a new door for
future research on accelerating transfer learning models.
Angiogenic Factors produced by Hypoxic Cells are a leading driver of Anastomoses in Sprouting Angiogenesis–a computational study
Authors: Maurício Moreira-Soares, Rita Coimbra, Luís Rebelo, João Carvalho, Rui D. M. Travasso
Date: 2018-06-07 00:00:00
Proceeding: Scientific Reports 2018 6

Angiogenesis - the growth of new blood vessels from a pre-existing vasculature - is key in both physiological processes and on several pathological scenarios such as cancer progression or diabetic retinopathy. For the new vascular networks to be functional, it is required that the growing sprouts merge either with an existing functional mature vessel or with another growing sprout. This process is called anastomosis. We present a systematic 2D and 3D computational study of vessel growth in a tissue to address the capability of angiogenic factor gradients to drive anastomosis formation. We consider that these growth factors are produced only by tissue cells in hypoxia, i.e. until nearby vessels merge and become capable of carrying blood and irrigating their vicinity. We demonstrate that this increased production of angiogenic factors by hypoxic cells is able to promote vessel anastomoses events in both 2D and 3D. The simulations also verify that the morphology of these networks has an increased resilience toward variations in the endothelial cell’s proliferation and chemotactic response. The distribution of tissue cells and the concentration of the growth factors they produce are the major factors in determining the final morphology of the network.
Multi-view to Novel view: Synthesizing Novel Views with Self-Learned Confidence
Authors: Shao-Hua Sun, Minyoung Huh, Yuan-Hong Liao, Ning Zhang, Joseph J. Lim
Date: 2018-10-05 00:00:00
Proceeding: Proceedings of the 15th European Conference on Computer Vision, 2018 10
Tasks: Novel View Synthesis

We address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images. We propose an end-to-end trainable framework that learns to exploit multiple viewpoints to synthesize a novel view without any 3D supervision. Specifically, our model consists of a flow prediction module and a pixel generation module to directly leverage information presented in source views as well as hallucinate missing pixels from statistical priors. To merge the predictions produced by the two modules given multi-view source images, we introduce a self-learned confidence aggregation mechanism. We evaluate our model on images rendered from 3D object models as well as real and synthesized scenes. We demonstrate that our model is able to achieve state-of-the-art results as well as progressively improve its predictions when more source images are available.
Detecting Human-Object Interactions via Functional Generalization
Authors: Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa
Date: 2019-04-05 00:00:00
Tasks: Human-Object Interaction Detection, Object

We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner. The proposed model is simple and efficiently uses the data, visual features of the human, relative spatial orientation of the human and the object, and the knowledge that functionally similar objects take part in similar interactions with humans. We provide extensive experimental validation for our approach and demonstrate state-of-the-art results for HOI detection. On the HICO-Det dataset our method achieves a gain of over 2.5% absolute points in mean average precision (mAP) over state-of-the-art. We also show that our approach leads to significant performance gains for zero-shot HOI detection in the seen object setting. We further demonstrate that using a generic object detector, our model can generalize to interactions involving previously unseen objects.
Reducing catastrophic forgetting when evolving neural networks
Authors: Joseph Early
Date: 2019-04-05 00:00:00
Tasks: Reinforcement Learning

A key stepping stone in the development of an artificial general intelligence
(a machine that can perform any task), is the production of agents that can
perform multiple tasks at once instead of just one. Unfortunately, canonical
methods are very prone to catastrophic forgetting (CF) - the act of overwriting
previous knowledge about a task when learning a new task. Recent efforts have
developed techniques for overcoming CF in learning systems, but no attempt has
been made to apply these new techniques to evolutionary systems. This research
presents a novel technique, weight protection, for reducing CF in evolutionary
systems by adapting a method from learning systems. It is used in conjunction
with other evolutionary approaches for overcoming CF and is shown to be
effective at alleviating CF when applied to a suite of reinforcement learning
tasks. It is speculated that this work could indicate the potential for a wider
application of existing learning-based approaches to evolutionary systems and
that evolutionary techniques may be competitive with or better than learning
systems when it comes to reducing CF.
Structured agents for physical construction
Authors: Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick
Date: 2019-04-05 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Scene Understanding

Physical construction---the ability to compose objects, subject to physical dynamics, to serve some function---is fundamental to human intelligence. We introduce a suite of challenging physical construction tasks inspired by how children play with blocks, such as matching a target configuration, stacking blocks to connect objects together, and creating shelter-like structures over target objects. We examine how a range of deep reinforcement learning agents fare on these challenges, and introduce several new approaches which provide superior performance. Our results show that agents which use structured representations (e.g., objects and scene graphs) and structured policies (e.g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes. Model-based agents which use Monte-Carlo Tree Search also outperform strictly model-free agents in our most challenging construction problems. We conclude that approaches which combine structured representations and reasoning with powerful learning are a key path toward agents that possess rich intuitive physics, scene understanding, and planning.
Moving Object Detection under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition
Authors: Moein Shakeri, Hong Zhang
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Moving Object Detection, object-detection, Object Detection

Although low-rank and sparse decomposition based methods have been
successfully applied to the problem of moving object detection using structured
sparsity-inducing norms, they are still vulnerable to significant illumination
changes that arise in certain applications. We are interested in moving object
detection in applications involving time-lapse image sequences for which
current methods mistakenly group moving objects and illumination changes into
foreground. Our method relies on the multilinear (tensor) data low-rank and
sparse decomposition framework to address the weaknesses of existing methods.
The key to our proposed method is to create first a set of prior maps that can
characterize the changes in the image sequence due to illumination. We show
that they can be detected by a k-support norm. To deal with concurrent, two
types of changes, we employ two regularization terms, one for detecting moving
objects and the other for accounting for illumination changes, in the tensor
low-rank and sparse decomposition formulation. Through comprehensive
experiments using challenging datasets, we show that our method demonstrates a
remarkable ability to detect moving objects under discontinuous change in
illumination, and outperforms the state-of-the-art solutions to this
challenging problem.
Improving Scientific Article Visibility by Neural Title Simplification
Authors: Alexander Shvets
Date: 2019-04-05 00:00:00
Tasks: Informativeness

The rapidly growing amount of data that scientific content providers should
deliver to a user makes them create effective recommendation tools. A title of
an article is often the only shown element to attract people's attention. We
offer an approach to automatic generating titles with various levels of
informativeness to benefit from different categories of users. Statistics from
ResearchGate used to bias train datasets and specially designed post-processing
step applied to neural sequence-to-sequence models allow reaching the desired
variety of simplified titles to gain a trade-off between the attractiveness and
transparency of recommendation.
Diversified Hidden Markov Models for Sequential Labeling
Authors: Maoying Qiao, Wei Bian, Richard Yida Xu, DaCheng Tao
Date: 2019-04-05 00:00:00
Tasks: Diversity, Optical Character Recognition, Optical Character Recognition (OCR), Part-Of-Speech Tagging, POS, POS Tagging

Labeling of sequential data is a prevalent meta-problem for a wide range of
real world applications. While the first-order Hidden Markov Models (HMM)
provides a fundamental approach for unsupervised sequential labeling, the basic
model does not show satisfying performance when it is directly applied to real
world problems, such as part-of-speech tagging (PoS tagging) and optical
character recognition (OCR). Aiming at improving performance, important
extensions of HMM have been proposed in the literatures. One of the common key
features in these extensions is the incorporation of proper prior information.
In this paper, we propose a new extension of HMM, termed diversified Hidden
Markov Models (dHMM), which utilizes a diversity-encouraging prior over the
state-transition probabilities and thus facilitates more dynamic sequential
labellings. Specifically, the diversity is modeled by a continuous
determinantal point process prior, which we apply to both unsupervised and
supervised scenarios. Learning and inference algorithms for dHMM are derived.
Empirical evaluations on benchmark datasets for unsupervised PoS tagging and
supervised OCR confirmed the effectiveness of dHMM, with competitive
performance to the state-of-the-art.
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects
Authors: Roman Kaskman, Sergey Zakharov, Ivan Shugurov, Slobodan Ilic
Date: 2019-04-05 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Object, Pose Estimation

Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such datasets is growing continuously. Despite the fact that some of exist, they are scarce and typically have restricted setups, such as a single object per sequence, or they focus on specific object types, such as textureless industrial parts. Besides, two significant components are often ignored: training using only available 3D models instead of real data and scalability, i.e. training one method to detect all objects rather than training one detector per object. Other challenges, such as occlusions, changing light conditions and changes in object appearance, as well precisely defined benchmarks are either not present or are scattered among different datasets. In this paper we present a dataset for 6D pose estimation that covers the above-mentioned challenges, mainly targeting training from 3D models (both textured and textureless), scalability, occlusions, and changes in light conditions and object appearance. The dataset features 33 objects (17 toy, 8 household and 8 industry-relevant objects) over 13 scenes of various difficulty. We also present a set of benchmarks to test various desired detector properties, particularly focusing on scalability with respect to the number of objects and resistance to changing light conditions, occlusions and clutter. We also set a baseline for the presented benchmarks using a state-of-the-art DPOD detector. Considering the difficulty of making such datasets, we plan to release the code allowing other researchers to extend this dataset or make their own datasets in the future.
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis
Authors: Laura Bostan, Roman Klinger
Date: 2019-04-05 00:00:00
Proceeding: WS 2019 6
Tasks: Emotion Classification, Emotion Recognition, General Classification, Word Embeddings

Adjective phrases like "a little bit surprised", "completely shocked", or
"not stunned at all" are not handled properly by currently published
state-of-the-art emotion classification and intensity prediction systems which
use pre-dominantly non-contextualized word embeddings as input. Based on this
finding, we analyze differences between embeddings used by these systems in
regard to their capability of handling such cases. Furthermore, we argue that
intensifiers in context of emotion words need special treatment, as is
established for sentiment polarity classification, but not for more
fine-grained emotion prediction. To resolve this issue, we analyze different
aspects of a post-processing pipeline which enriches the word representations
of such phrases. This includes expansion of semantic spaces at the phrase level
and sub-word level followed by retrofitting to emotion lexica. We evaluate the
impact of these steps with A La Carte and Bag-of-Substrings extensions based on
pretrained GloVe, Word2vec, and fastText embeddings against a crowd-sourced
corpus of intensity annotations for tweets containing our focus phrases. We
show that the fastText-based models do not gain from handling these specific
phrases under inspection. For Word2vec embeddings, we show that our
post-processing pipeline improves the results by up to 8% on a novel dataset
densely populated with intensifiers.