pwc-archive/papers-with-abstracts huggingface dataset:
Learning through deterministic assignment of hidden parameters
Authors: Jian Fang, Shao-Bo Lin, Zongben Xu
Date: 2018-03-22 00:00:00

Supervised learning frequently boils down to determining hidden and bright
parameters in a parameterized hypothesis space based on finite input-output
samples. The hidden parameters determine the attributions of hidden predictors
or the nonlinear mechanism of an estimator, while the bright parameters
characterize how hidden predictors are linearly combined or the linear
mechanism. In traditional learning paradigm, hidden and bright parameters are
not distinguished and trained simultaneously in one learning process. Such an
one-stage learning (OSL) brings a benefit of theoretical analysis but suffers
from the high computational burden. To overcome this difficulty, a two-stage
learning (TSL) scheme, featured by learning through deterministic assignment of
hidden parameters (LtDaHP) was proposed, which suggests to deterministically
generate the hidden parameters by using minimal Riesz energy points on a sphere
and equally spaced points in an interval. We theoretically show that with such
deterministic assignment of hidden parameters, LtDaHP with a neural network
realization almost shares the same generalization performance with that of OSL.
We also present a series of simulations and application examples to support the
outperformance of LtDaHP
Gradient Descent Quantizes ReLU Network Features
Authors: Hartmut Maennel, Olivier Bousquet, Sylvain Gelly
Date: 2018-03-22 00:00:00
Tasks: Quantization

Deep neural networks are often trained in the over-parametrized regime (i.e.
with far more parameters than training examples), and understanding why the
training converges to solutions that generalize remains an open problem.
Several studies have highlighted the fact that the training procedure, i.e.
mini-batch Stochastic Gradient Descent (SGD) leads to solutions that have
specific properties in the loss landscape. However, even with plain Gradient
Descent (GD) the solutions found in the over-parametrized regime are pretty
good and this phenomenon is poorly understood.
  We propose an analysis of this behavior for feedforward networks with a ReLU
activation function under the assumption of small initialization and learning
rate and uncover a quantization effect: The weight vectors tend to concentrate
at a small number of directions determined by the input data. As a consequence,
we show that for given input data there are only finitely many, "simple"
functions that can be obtained, independent of the network size. This puts
these functions in analogy to linear interpolations (for given input data there
are finitely many triangulations, which each determine a function by linear
interpolation). We ask whether this analogy extends to the generalization
properties - while the usual distribution-independent generalization property
does not hold, it could be that for e.g. smooth functions with bounded second
derivative an approximation property holds which could "explain" generalization
of networks (of unbounded size) to unseen inputs.
Dynamic Filtering with Large Sampling Field for ConvNets
Authors: Jialin Wu, Dai Li, Yu Yang, Chandrajit Bajaj, Xiangyang Ji
Date: 2018-03-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: object-detection, Object Detection, Position, Semantic Segmentation, Translation

We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. Such multiple samples enlarge the kernels' receptive fields significantly without requiring more parameters. While LS-DFN inherits the advantages of DFN, namely avoiding feature map blurring by position-wise kernels while keeping translation invariance, it also efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs. Our model is efficient and can be trained end-to-end via standard back-propagation. We demonstrate the merits of our LS-DFN on both sparse and dense prediction tasks involving object detection, semantic segmentation, and flow estimation. Our results show LS-DFN enjoys stronger recognition abilities in object detection and semantic segmentation tasks on VOC benchmark and sharper responses in flow estimation on FlyingChairs dataset compared to strong baselines.
A framework for Culture-aware Robots based on Fuzzy Logic
Authors: Barbara Bruno, Fulvio Mastrogiovanni, Federico Pecora, Antonio Sgorbissa, Alessandro Saffiotti
Date: 2018-03-22 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Cultural adaptation, i.e., the matching of a robot's behaviours to the
cultural norms and preferences of its user, is a well known key requirement for
the success of any assistive application. However, culture-dependent robot
behaviours are often implicitly set by designers, thus not allowing for an easy
and automatic adaptation to different cultures. This paper presents a method
for the design of culture-aware robots, that can automatically adapt their
behaviour to conform to a given culture. We propose a mapping from cultural
factors to related parameters of robot behaviours which relies on linguistic
variables to encode heterogeneous cultural factors in a uniform formalism, and
on fuzzy rules to encode qualitative relations among multiple variables. We
illustrate the approach in two practical case studies.
What do Deep Networks Like to See?
Authors: Sebastian Palacio, Joachim Folz, Jörn Hees, Federico Raue, Damian Borth, Andreas Dengel
Date: 2018-03-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Classification

We propose a novel way to measure and understand convolutional neural
networks by quantifying the amount of input signal they let in. To do this, an
autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with
fixed parameters. We compared the reconstructed samples from AEs that were
fine-tuned on a set of image classifiers (AlexNet, VGG16, ResNet-50, and
Inception~v3) and found substantial differences. The AE learns which aspects of
the input space to preserve and which ones to ignore, based on the information
encoded in the backpropagated gradients. Measuring the changes in accuracy when
the signal of one classifier is used by a second one, a relation of total order
emerges. This order depends directly on each classifier's input signal but it
does not correlate with classification accuracy or network size. Further
evidence of this phenomenon is provided by measuring the normalized mutual
information between original images and auto-encoded reconstructions from
different fine-tuned AEs. These findings break new ground in the area of neural
network understanding, opening a new way to reason, debug, and interpret their
results. We present four concrete examples in the literature where observations
can now be explained in terms of the input signal that a model uses.
Robust and Parallel Bayesian Model Selection
Authors: Michael Minyi Zhang, Henry Lam, Lizhen Lin
Date: 2016-10-19 00:00:00
Tasks: model, Model Selection, Variable Selection

Effective and accurate model selection is an important problem in modern data
analysis. One of the major challenges is the computational burden required to
handle large data sets that cannot be stored or processed on one machine.
Another challenge one may encounter is the presence of outliers and
contaminations that damage the inference quality. The parallel "divide and
conquer" model selection strategy divides the observations of the full data set
into roughly equal subsets and perform inference and model selection
independently on each subset. After local subset inference, this method
aggregates the posterior model probabilities or other model/variable selection
criteria to obtain a final model by using the notion of geometric median. This
approach leads to improved concentration in finding the "correct" model and
model parameters and also is provably robust to outliers and data
contamination.
Boosting Adversarial Attacks with Momentum
Authors: Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li
Date: 2017-10-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Adversarial Attack

Deep neural networks are vulnerable to adversarial examples, which poses
security concerns on these algorithms due to the potentially severe
consequences. Adversarial attacks serve as an important surrogate to evaluate
the robustness of deep learning models before they are deployed. However, most
of existing adversarial attacks can only fool a black-box model with a low
success rate. To address this issue, we propose a broad class of momentum-based
iterative algorithms to boost adversarial attacks. By integrating the momentum
term into the iterative process for attacks, our methods can stabilize update
directions and escape from poor local maxima during the iterations, resulting
in more transferable adversarial examples. To further improve the success rates
for black-box attacks, we apply momentum iterative algorithms to an ensemble of
models, and show that the adversarially trained models with a strong defense
ability are also vulnerable to our black-box attacks. We hope that the proposed
methods will serve as a benchmark for evaluating the robustness of various deep
models and defense methods. With this method, we won the first places in NIPS
2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack
competitions.
Revisiting Gray Pixel for Statistical Illumination Estimation
Authors: Yanlin Qian, Said Pertuz, Jarno Nikkanen, Joni-Kristian Kämäräinen, Jiri Matas
Date: 2018-03-22 00:00:00
Tasks: Clustering, Color Constancy

We present a statistical color constancy method that relies on novel gray
pixel detection and mean shift clustering. The method, called Mean Shifted Grey
Pixel -- MSGP, is based on the observation: true-gray pixels are aligned
towards one single direction. Our solution is compact, easy to compute and
requires no training. Experiments on two real-world benchmarks show that the
proposed approach outperforms state-of-the-art methods in the camera-agnostic
scenario. In the setting where the camera is known, MSGP outperforms all
statistical methods.
Recurrent Neural Networks for Semantic Instance Segmentation
Authors: Amaia Salvador, Miriam Bellver, Victor Campos, Manel Baradad, Ferran Marques, Jordi Torres, Xavier Giro-i-Nieto
Date: 2017-12-02 00:00:00
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation

We present a recurrent model for semantic instance segmentation that
sequentially generates binary masks and their associated class probabilities
for every object in an image. Our proposed system is trainable end-to-end from
an input image to a sequence of labeled masks and, compared to methods relying
on object proposals, does not require post-processing steps on its output. We
study the suitability of our recurrent model on three different instance
segmentation benchmarks, namely Pascal VOC 2012, CVPPP Plant Leaf Segmentation
and Cityscapes. Further, we analyze the object sorting patterns generated by
our model and observe that it learns to follow a consistent pattern, which
correlates with the activations learned in the encoder part of our network.
Source code and models are available at https://imatge-upc.github.io/rsis/
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
Authors: Manuel Carbonell, Mauricio Villegas, Alicia Fornés, Josep Lladós
Date: 2018-03-16 00:00:00
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Transfer Learning

When extracting information from handwritten documents, text transcription
and named entity recognition are usually faced as separate subsequent tasks.
This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks
jointly, using a single neural network with a common architecture used for
plain text recognition. Experimentally, the work has been tested on a
collection of historical marriage records. Results of experiments are presented
to show the effect on the performance for different configurations: different
ways of encoding the information, doing or not transfer learning and processing
at text line or multi-line region level. The results are comparable to state of
the art reported in the ICDAR 2017 Information Extraction competition, even
though the proposed technique does not use any dictionaries, language modeling
or post processing.
Style Aggregated Network for Facial Landmark Detection
Authors: Xuanyi Dong, Yan Yan, Wanli Ouyang, Yi Yang
Date: 2018-03-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Alignment, Facial Landmark Detection

Recent advances in facial landmark detection achieve success by learning
discriminative features from rich deformation of face shapes and poses. Besides
the variance of faces themselves, the intrinsic variance of image styles, e.g.,
grayscale vs. color images, light vs. dark, intense vs. dull, and so on, has
constantly been overlooked. This issue becomes inevitable as increasing web
images are collected from various sources for training neural networks. In this
work, we propose a style-aggregated approach to deal with the large intrinsic
variance of image styles for facial landmark detection. Our method transforms
original face images to style-aggregated images by a generative adversarial
module. The proposed scheme uses the style-aggregated image to maintain face
images that are more robust to environmental changes. Then the original face
images accompanying with style-aggregated ones play a duet to train a landmark
detector which is complementary to each other. In this way, for each face, our
method takes two images as input, i.e., one in its original style and the other
in the aggregated style. In experiments, we observe that the large variance of
image styles would degenerate the performance of facial landmark detectors.
Moreover, we show the robustness of our method to the large variance of image
styles by comparing to a variant of our approach, in which the generative
adversarial module is removed, and no style-aggregated images are used. Our
approach is demonstrated to perform well when compared with state-of-the-art
algorithms on benchmark datasets AFLW and 300-W. Code is publicly available on
GitHub: https://github.com/D-X-Y/SAN
Prioritized Multi-View Stereo Depth Map Generation Using Confidence Prediction
Authors: Christian Mostegel, Friedrich Fraundorfer, Horst Bischof
Date: 2018-03-22 00:00:00
Tasks: 3D Reconstruction

In this work, we propose a novel approach to prioritize the depth map
computation of multi-view stereo (MVS) to obtain compact 3D point clouds of
high quality and completeness at low computational cost. Our prioritization
approach operates before the MVS algorithm is executed and consists of two
steps. In the first step, we aim to find a good set of matching partners for
each view. In the second step, we rank the resulting view clusters (i.e. key
views with matching partners) according to their impact on the fulfillment of
desired quality parameters such as completeness, ground resolution and
accuracy. Additional to geometric analysis, we use a novel machine learning
technique for training a confidence predictor. The purpose of this confidence
predictor is to estimate the chances of a successful depth reconstruction for
each pixel in each image for one specific MVS algorithm based on the RGB images
and the image constellation. The underlying machine learning technique does not
require any ground truth or manually labeled data for training, but instead
adapts ideas from depth map fusion for providing a supervision signal. The
trained confidence predictor allows us to evaluate the quality of image
constellations and their potential impact to the resulting 3D reconstruction
and thus builds a solid foundation for our prioritization approach. In our
experiments, we are thus able to reach more than 70% of the maximal reachable
quality fulfillment using only 5% of the available images as key views. For
evaluating our approach within and across different domains, we use two
completely different scenarios, i.e. cultural heritage preservation and
reconstruction of single family houses.
Long-Term Visual Object Tracking Benchmark
Authors: Abhinav Moudgil, Vineet Gandhi
Date: 2017-12-04 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking, Visual Tracking

We propose a new long video dataset (called Track Long and Prosper - TLP) and
benchmark for single object tracking. The dataset consists of 50 HD videos from
real world scenarios, encompassing a duration of over 400 minutes (676K
frames), making it more than 20 folds larger in average duration per sequence
and more than 8 folds larger in terms of total covered duration, as compared to
existing generic datasets for visual tracking. The proposed dataset paves a way
to suitably assess long term tracking performance and train better deep
learning architectures (avoiding/reducing augmentation, which may not reflect
real world behaviour). We benchmark the dataset on 17 state of the art trackers
and rank them according to tracking accuracy and run time speeds. We further
present thorough qualitative and quantitative evaluation highlighting the
importance of long term aspect of tracking. Our most interesting observations
are (a) existing short sequence benchmarks fail to bring out the inherent
differences in tracking algorithms which widen up while tracking on long
sequences and (b) the accuracy of trackers abruptly drops on challenging long
sequences, suggesting the potential need of research efforts in the direction
of long-term tracking.
Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data
Authors: Xihui Liu, Hongsheng Li, Jing Shao, Dapeng Chen, Xiaogang Wang
Date: 2018-03-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Captioning, Retrieval

The aim of image captioning is to generate captions by machine to describe
image contents. Despite many efforts, generating discriminative captions for
images remains non-trivial. Most traditional approaches imitate the language
structure patterns, thus tend to fall into a stereotype of replicating frequent
phrases or sentences and neglect unique aspects of each image. In this work, we
propose an image captioning framework with a self-retrieval module as training
guidance, which encourages generating discriminative captions. It brings unique
advantages: (1) the self-retrieval guidance can act as a metric and an
evaluator of caption discriminativeness to assure the quality of generated
captions. (2) The correspondence between generated captions and images are
naturally incorporated in the generation process without human annotations, and
hence our approach could utilize a large amount of unlabeled images to boost
captioning performance with no additional laborious annotations. We demonstrate
the effectiveness of the proposed retrieval-guided method on COCO and Flickr30k
captioning datasets, and show its superior captioning performance with more
discriminative captions.
Reducing Complexity of HEVC: A Deep Learning Approach
Authors: Mai Xu, Tianyi Li, Zulin Wang, Xin Deng, Ren Yang, Zhenyu Guan
Date: 2017-09-19 00:00:00
Tasks: Deep Learning

High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the
proceeding H.264 standard but at the expense of extremely high encoding
complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a
large proportion of the HEVC encoding complexity, due to the bruteforce search
for rate-distortion optimization (RDO). Therefore, this paper proposes a deep
learning approach to predict the CU partition for reducing the HEVC complexity
at both intra- and inter-modes, which is based on convolutional neural network
(CNN) and long- and short-term memory (LSTM) network. First, we establish a
large-scale database including substantial CU partition data for HEVC intra-
and inter-modes. This enables deep learning on the CU partition. Second, we
represent the CU partition of an entire coding tree unit (CTU) in the form of a
hierarchical CU partition map (HCPM). Then, we propose an early-terminated
hierarchical CNN (ETH-CNN) for learning to predict the HCPM. Consequently, the
encoding complexity of intra-mode HEVC can be drastically reduced by replacing
the brute-force search with ETH-CNN to decide the CU partition. Third, an
early-terminated hierarchical LSTM (ETH-LSTM) is proposed to learn the temporal
correlation of the CU partition. Then, we combine ETH-LSTM and ETH-CNN to
predict the CU partition for reducing the HEVC complexity for inter-mode.
Finally, experimental results show that our approach outperforms other
state-of-the-art approaches in reducing the HEVC complexity at both intra- and
inter-modes.
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Authors: Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox
Date: 2018-01-19 00:00:00
Tasks: Optical Flow Estimation

The finding that very large networks can be trained efficiently and reliably
has led to a paradigm shift in computer vision from engineered solutions to
learning formulations. As a result, the research challenge shifts from devising
algorithms to creating suitable and abundant training data for supervised
learning. How to efficiently create such training data? The dominant data
acquisition method in visual recognition is based on web data and manual
annotation. Yet, for many computer vision problems, such as stereo or optical
flow estimation, this approach is not feasible because humans cannot manually
enter a pixel-accurate flow field. In this paper, we promote the use of
synthetically generated data for the purpose of training deep networks on such
tasks.We suggest multiple ways to generate such data and evaluate the influence
of dataset properties on the performance and generalization properties of the
resulting networks. We also demonstrate the benefit of learning schedules that
use different types of data at selected stages of the training process.
Learning-based Model Predictive Control for Safe Exploration
Authors: Torsten Koller, Felix Berkenkamp, Matteo Turchetta, Andreas Krause
Date: 2018-03-22 00:00:00
Tasks: model, Model Predictive Control, Safe Exploration

Learning-based methods have been successful in solving complex control tasks
without significant prior knowledge about the system. However, these methods
typically do not provide any safety guarantees, which prevents their use in
safety-critical, real-world applications. In this paper, we present a
learning-based model predictive control scheme that can provide provable
high-probability safety guarantees. To this end, we exploit regularity
assumptions on the dynamics in terms of a Gaussian process prior to construct
provably accurate confidence intervals on predicted trajectories. Unlike
previous approaches, we do not assume that model uncertainties are independent.
Based on these predictions, we guarantee that trajectories satisfy safety
constraints. Moreover, we use a terminal set constraint to recursively
guarantee the existence of safe control actions at every iteration. In our
experiments, we show that the resulting algorithm can be used to safely and
efficiently explore and learn about dynamic systems.