pwc-archive/papers-with-abstracts huggingface dataset:
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems
Authors: Ravi Kiran Sarvadevabhatla, Shanthakumar Venkatraman, R. Venkatesh Babu
Date: 2016-11-23 00:00:00
Tasks: Benchmarking, Object, Object Recognition, Semantic Similarity, Semantic Textual Similarity

An examination of object recognition challenge leaderboards (ILSVRC,
PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small
differences amongst themselves in terms of error rate/mAP. To better
differentiate the top performers, additional criteria are required. Moreover,
the (test) images, on which the performance scores are based, predominantly
contain fully visible objects. Therefore, `harder' test images, mimicking the
challenging conditions (e.g. occlusion) in which humans routinely recognize
objects, need to be utilized for benchmarking. To address the concerns
mentioned above, we make two contributions. First, we systematically vary the
level of local object-part content, global detail and spatial context in images
from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12.
Second, we propose an object-part based benchmarking procedure which quantifies
classifiers' robustness to a range of visibility and contextual settings. The
benchmarking procedure relies on a semantic similarity measure that naturally
addresses potential semantic granularity differences between the category
labels in training and test datasets, thus eliminating manual mapping. We use
our procedure on the PPSS-12 dataset to benchmark top-performing classifiers
trained on the ILSVRC-2012 dataset. Our results show that the proposed
benchmarking procedure enables additional differentiation among
state-of-the-art object classifiers in terms of their ability to handle missing
content and insufficient object detail. Given this capability for additional
differentiation, our approach can potentially supplement existing benchmarking
procedures used in object recognition challenge leaderboards.
An Analytic Expression of Relative Approximation Error for a Class of Evolutionary Algorithms
Authors: Jun He
Date: 2015-11-11 00:00:00
Tasks: Evolutionary Algorithms

An important question in evolutionary computation is how good solutions
evolutionary algorithms can produce. This paper aims to provide an analytic
analysis of solution quality in terms of the relative approximation error,
which is defined by the error between 1 and the approximation ratio of the
solution found by an evolutionary algorithm. Since evolutionary algorithms are
iterative methods, the relative approximation error is a function of
generations. With the help of matrix analysis, it is possible to obtain an
exact expression of such a function. In this paper, an analytic expression for
calculating the relative approximation error is presented for a class of
evolutionary algorithms, that is, (1+1) strictly elitist evolution algorithms.
Furthermore, analytic expressions of the fitness value and the average
convergence rate in each generation are also derived for this class of
evolutionary algorithms. The approach is promising, and it can be extended to
non-elitist or population-based algorithms too.
Predicting the dynamics of 2d objects with a deep residual network
Authors: François Fleuret
Date: 2016-10-13 00:00:00
Tasks: Image-to-Image Regression

We investigate how a residual network can learn to predict the dynamics of
interacting shapes purely as an image-to-image regression task.
  With a simple 2d physics simulator, we generate short sequences composed of
rectangles put in motion by applying a pulling force at a point picked at
random. The network is trained with a quadratic loss to predict the image of
the resulting configuration, given the image of the starting configuration and
an image indicating the point of grasping.
  Experiments show that the network learns to predict accurately the resulting
image, which implies in particular that (1) it segments rectangles as distinct
components, (2) it infers which one contains the grasping point, (3) it models
properly the dynamic of a single rectangle, including the torque, (4) it
detects and handles collisions to some extent, and (5) it re-synthesizes
properly the entire scene with displaced rectangles.
Question Retrieval for Community-based Question Answering via Heterogeneous Network Integration Learning
Authors: Zheqian Chen, Chi Zhang, Zhou Zhao, Deng Cai
Date: 2016-11-24 00:00:00
Tasks: Question Answering, Retrieval

Community based question answering platforms have attracted substantial users
to share knowledge and learn from each other. As the rapid enlargement of CQA
platforms, quantities of overlapped questions emerge, which makes users
confounded to select a proper reference. It is urgent for us to take effective
automated algorithms to reuse historical questions with corresponding answers.
In this paper we focus on the problem with question retrieval, which aims to
match historical questions that are relevant or semantically equivalent to
resolve one s query directly. The challenges in this task are the lexical gaps
between questions for the word ambiguity and word mismatch problem.
Furthermore, limited words in queried sentences cause sparsity of word
features. To alleviate these challenges, we propose a novel framework named
HNIL which encodes not only the question contents but also the askers social
interactions to enhance the question embedding performance. More specifically,
we apply random walk based learning method with recurrent neural network to
match the similarities between askers question and historical questions
proposed by other users. Extensive experiments on a large scale dataset from a
real world CQA site show that employing the heterogeneous social network
information outperforms the other state of the art solutions in this task.
Comparative study of histogram distance measures for re-identification
Authors: Pedro A. Marín-Reyes, Javier Lorenzo-Navarro, Modesto Castrillón-Santana
Date: 2016-11-24 00:00:00

Color based re-identification methods usually rely on a distance function to
measure the similarity between individuals. In this paper we study the behavior
of several histogram distance measures in different color spaces. We wonder
whether there is a particular histogram distance measure better than others,
likewise also, if there is a color space that present better discrimination
features. Several experiments are designed and evaluated in several images to
obtain measures against various color spaces. We test in several image
databases. A measure ranking is generated to calculate the area under the CMC,
this area is the indicator used to evaluate which distance measure and color
space present the best performance for the considered databases. Also, other
parameters such as the image division in horizontal stripes and number of
histogram bins, have been studied.
Extraction of airway trees using multiple hypothesis tracking and template matching
Authors: Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne
Date: 2016-11-24 00:00:00
Tasks: Template Matching

Knowledge of airway tree morphology has important clinical applications in
diagnosis of chronic obstructive pulmonary disease. We present an automatic
tree extraction method based on multiple hypothesis tracking and template
matching for this purpose and evaluate its performance on chest CT images. The
method is adapted from a semi-automatic method devised for vessel segmentation.
Idealized tubular templates are constructed that match airway probability
obtained from a trained classifier and ranked based on their relative
significance. Several such regularly spaced templates form the local hypotheses
used in constructing a multiple hypothesis tree, which is then traversed to
reach decisions. The proposed modifications remove the need for local
thresholding of hypotheses as decisions are made entirely based on statistical
comparisons involving the hypothesis tree. The results show improvements in
performance when compared to the original method and region growing on
intensity images. We also compare the method with region growing on the
probability images, where the presented method does not show substantial
improvement, but we expect it to be less sensitive to local anomalies in the
data.
Ask Your Neurons: A Deep Learning Approach to Visual Question Answering
Authors: Mateusz Malinowski, Marcus Rohrbach, Mario Fritz
Date: 2016-05-09 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Ask Your Neurons, a scalable, jointly
trained, end-to-end formulation to this problem.
  In contrast to previous efforts, we are facing a multi-modal problem where
the language output (answer) is conditioned on visual and natural language
inputs (image and question). We provide additional insights into the problem by
analyzing how much information is contained only in the language part for which
we provide a new human baseline. To study human consensus, which is related to
the ambiguities inherent in this challenging task, we propose two novel metrics
and collect additional answers which extend the original DAQUAR dataset to
DAQUAR-Consensus.
  Moreover, we also extend our analysis to VQA, a large-scale question
answering about images dataset, where we investigate some particular design
choices and show the importance of stronger visual models. At the same time, we
achieve strong performance of our model that still uses a global image
representation. Finally, based on such analysis, we refine our Ask Your Neurons
on DAQUAR, which also leads to a better performance on this challenging task.
Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models
Authors: Shengyong Ding, Junyu Wu, Wei Xu, Hongyang Chao
Date: 2016-11-24 00:00:00
Tasks: Face Model, Face Recognition

Training data are critical in face recognition systems. However, labeling a
large scale face data for a particular domain is very tedious. In this paper,
we propose a method to automatically and incrementally construct datasets from
massive weakly labeled data of the target domain which are readily available on
the Internet under the help of a pretrained face model. More specifically,
given a large scale weakly labeled dataset in which each face image is
associated with a label, i.e. the name of an identity, we create a graph for
each identity with edges linking matched faces verified by the existing model
under a tight threshold. Then we use the maximal subgraph as the cleaned data
for that identity. With the cleaned dataset, we update the existing face model
and use the new model to filter the original dataset to get a larger cleaned
dataset. We collect a large weakly labeled dataset containing 530,560 Asian
face images of 7,962 identities from the Internet, which will be published for
the study of face recognition. By running the filtering process, we obtain a
cleaned datasets (99.7+% purity) of size 223,767 (recall 70.9%). On our testing
dataset of Asian faces, the model trained by the cleaned dataset achieves
recognition rate 93.1%, which obviously outperforms the model trained by the
public dataset CASIA whose recognition rate is 85.9%.
Quantum Enhanced Inference in Markov Logic Networks
Authors: Peter Wittek, Christian Gogolin
Date: 2016-11-24 00:00:00
Tasks: BIG-bench Machine Learning

Markov logic networks (MLNs) reconcile two opposing schools in machine
learning and artificial intelligence: causal networks, which account for
uncertainty extremely well, and first-order logic, which allows for formal
deduction. An MLN is essentially a first-order logic template to generate
Markov networks. Inference in MLNs is probabilistic and it is often performed
by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling.
An MLN has many regular, symmetric structures that can be exploited at both
first-order level and in the generated Markov network. We analyze the graph
structures that are produced by various lifting methods and investigate the
extent to which quantum protocols can be used to speed up Gibbs sampling with
state preparation and measurement schemes. We review different such approaches,
discuss their advantages, theoretical limitations, and their appeal to
implementations. We find that a straightforward application of a recent result
yields exponential speedup compared to classical heuristics in approximate
probabilistic inference, thereby demonstrating another example where advanced
quantum resources can potentially prove useful in machine learning.
Double-quantitative $γ^{\ast}-$fuzzy coverings approximation operators
Authors: Guangming Lang
Date: 2016-11-24 00:00:00

In digital-based information boom, the fuzzy covering rough set model is an
important mathematical tool for artificial intelligence, and how to build the
bridge between the fuzzy covering rough set theory and Pawlak's model is
becoming a hot research topic. In this paper, we first present the
$\gamma-$fuzzy covering based probabilistic and grade approximation operators
and double-quantitative approximation operators. We also study the
relationships among the three types of $\gamma-$fuzzy covering based
approximation operators. Second, we propose the $\gamma^{\ast}-$fuzzy coverings
based multi-granulation probabilistic and grade lower and upper approximation
operators and multi-granulation double-quantitative lower and upper
approximation operators. We also investigate the relationships among these
types of $\gamma-$fuzzy coverings based approximation operators. Finally, we
employ several examples to illustrate how to construct the lower and upper
approximations of fuzzy sets with the absolute and relative quantitative
information.
User Personalized Satisfaction Prediction via Multiple Instance Deep Learning
Authors: Zheqian Chen, Ben Gao, Huimin Zhang, Zhou Zhao, Deng Cai
Date: 2016-11-24 00:00:00
Tasks: Deep Learning, feature selection, Multiple Instance Learning, Prediction, Question Answering, Weakly-supervised Learning

Community based question answering services have arisen as a popular
knowledge sharing pattern for netizens. With abundant interactions among users,
individuals are capable of obtaining satisfactory information. However, it is
not effective for users to attain answers within minutes. Users have to check
the progress over time until the satisfying answers submitted. We address this
problem as a user personalized satisfaction prediction task. Existing methods
usually exploit manual feature selection. It is not desirable as it requires
careful design and is labor intensive. In this paper, we settle this issue by
developing a new multiple instance deep learning framework. Specifically, in
our settings, each question follows a weakly supervised learning multiple
instance learning assumption, where its obtained answers can be regarded as
instance sets and we define the question resolved with at least one
satisfactory answer. We thus design an efficient framework exploiting multiple
instance learning property with deep learning to model the question answer
pairs. Extensive experiments on large scale datasets from Stack Exchange
demonstrate the feasibility of our proposed framework in predicting askers
personalized satisfaction. Our framework can be extended to numerous
applications such as UI satisfaction Prediction, multi armed bandit problem,
expert finding and so on.
Deep Joint Face Hallucination and Recognition
Authors: Junyu Wu, Shengyong Ding, Wei Xu, Hongyang Chao
Date: 2016-11-24 00:00:00
Tasks: Face Hallucination, Face Recognition, Hallucination

Deep models have achieved impressive performance for face hallucination
tasks. However, we observe that directly feeding the hallucinated facial images
into recog- nition models can even degrade the recognition performance despite
the much better visualization quality. In this paper, we address this problem
by jointly learning a deep model for two tasks, i.e. face hallucination and
recognition. In particular, we design an end-to-end deep convolution network
with hallucination sub-network cascaded by recognition sub-network. The
recognition sub- network are responsible for producing discriminative feature
representations using the hallucinated images as inputs generated by
hallucination sub-network. During training, we feed LR facial images into the
network and optimize the parameters by minimizing two loss items, i.e. 1) face
hallucination loss measured by the pixel wise difference between the ground
truth HR images and network-generated images; and 2) verification loss which is
measured by the classification error and intra-class distance. We extensively
evaluate our method on LFW and YTF datasets. The experimental results show that
our method can achieve recognition accuracy 97.95% on 4x down-sampled LFW
testing set, outperforming the accuracy 96.35% of conventional face recognition
model. And on the more challenging YTF dataset, we achieve recognition accuracy
90.65%, a margin over the recognition accuracy 89.45% obtained by conventional
face recognition model on the 4x down-sampled version.
Variational Autoencoders for Semi-supervised Text Classification
Authors: Weidi Xu, Haoze Sun, Chao Deng, Ying Tan
Date: 2016-03-08 00:00:00
Tasks: Classification, Decoder, General Classification, image-classification, Image Classification, Reinforcement Learning, Semi-Supervised Text Classification, text-classification, Text Classification

Although semi-supervised variational autoencoder (SemiVAE) works in image
classification task, it fails in text classification task if using vanilla LSTM
as its decoder. From a perspective of reinforcement learning, it is verified
that the decoder's capability to distinguish between different categorical
labels is essential. Therefore, Semi-supervised Sequential Variational
Autoencoder (SSVAE) is proposed, which increases the capability by feeding
label into its decoder RNN at each time-step. Two specific decoder structures
are investigated and both of them are verified to be effective. Besides, in
order to reduce the computational complexity in training, a novel optimization
method is proposed, which estimates the gradient of the unlabeled objective
function by sampling, along with two variance reduction techniques.
Experimental results on Large Movie Review Dataset (IMDB) and AG's News corpus
show that the proposed approach significantly improves the classification
accuracy compared with pure-supervised classifiers, and achieves competitive
performance against previous advanced methods. State-of-the-art results can be
obtained by integrating other pretraining-based methods.
Survey of Expressivity in Deep Neural Networks
Authors: Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein
Date: 2016-11-24 00:00:00
Tasks: Survey

We survey results on neural network expressivity described in "On the
Expressive Power of Deep Neural Networks". The paper motivates and develops
three natural measures of expressiveness, which all display an exponential
dependence on the depth of the network. In fact, all of these measures are
related to a fourth quantity, trajectory length. This quantity grows
exponentially in the depth of the network, and is responsible for the depth
sensitivity observed. These results translate to consequences for networks
during and after training. They suggest that parameters earlier in a network
have greater influence on its expressive power -- in particular, given a layer,
its influence on expressivity is determined by the remaining depth of the
network after that layer. This is verified with experiments on MNIST and
CIFAR-10. We also explore the effect of training on the input-output map, and
find that it trades off between the stability and expressivity.
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets
Authors: Jung-Su Ha, Han-Lim Choi
Date: 2016-11-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work presents a multiscale framework to solve an inverse reinforcement
learning (IRL) problem for continuous-time/state stochastic systems. We take
advantage of a diffusion wavelet representation of the associated Markov chain
to abstract the state space. This not only allows for effectively handling the
large (and geometrically complex) decision space but also provides more
interpretable representations of the demonstrated state trajectories and also
of the resulting policy of IRL. In the proposed framework, the problem is
divided into the global and local IRL, where the global approximation of the
optimal value functions are obtained using coarse features and the local
details are quantified using fine local features. An illustrative numerical
example on robot path control in a complex environment is presented to verify
the proposed method.
Watch What You Just Said: Image Captioning with Text-Conditional Attention
Authors: Luowei Zhou, Chenliang Xu, Parker Koch, Jason J. Corso
Date: 2016-06-15 00:00:00
Tasks: Image Captioning, Language Modeling, Language Modelling

Attention mechanisms have attracted considerable interest in image captioning
due to its powerful performance. However, existing methods use only visual
content as attention and whether textual context can improve attention in image
captioning remains unsolved. To explore this problem, we propose a novel
attention mechanism, called \textit{text-conditional attention}, which allows
the caption generator to focus on certain image features given previously
generated text. To obtain text-related image features for our attention model,
we adopt the guiding Long Short-Term Memory (gLSTM) captioning architecture
with CNN fine-tuning. Our proposed method allows joint learning of the image
embedding, text embedding, text-conditional attention and language model with
one network architecture in an end-to-end manner. We perform extensive
experiments on the MS-COCO dataset. The experimental results show that our
method outperforms state-of-the-art captioning methods on various quantitative
metrics as well as in human evaluation, which supports the use of our
text-conditional attention in image captioning.
Recalling Holistic Information for Semantic Segmentation
Authors: Hexiang Hu, Zhiwei Deng, Guang-Tong Zhou, Fei Sha, Greg Mori
Date: 2016-11-24 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation requires a detailed labeling of image pixels by object
category. Information derived from local image patches is necessary to describe
the detailed shape of individual objects. However, this information is
ambiguous and can result in noisy labels. Global inference of image content can
instead capture the general semantic concepts present. We advocate that
high-recall holistic inference of image concepts provides valuable information
for detailed pixel labeling. We build a two-stream neural network architecture
that facilitates information flow from holistic information to local pixels,
while keeping common image features shared among the low-level layers of both
the holistic analysis and segmentation branches. We empirically evaluate our
network on four standard semantic segmentation datasets. Our network obtains
state-of-the-art performance on PASCAL-Context and NYUDv2, and ablation studies
verify its effectiveness on ADE20K and SIFT-Flow.