pwc-archive/papers-with-abstracts huggingface dataset:
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Authors: Andy Zeng, Shuran Song, Kuan-Ting Yu, Elliott Donlon, Francois R. Hogan, Maria Bauza, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, Nima Fazeli, Ferran Alet, Nikhil Chavan Dafle, Rachel Holladay, Isabella Morona, Prem Qu Nair, Druck Green, Ian Taylor, Weber Liu, Thomas Funkhouser, Alberto Rodriguez
Date: 2017-10-03 00:00:00
Tasks: image-classification, Image Classification, Robotic Grasping

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu
Sampling Correctors
Authors: Clément Canonne, Themis Gouleakis, Ronitt Rubinfeld
Date: 2015-04-24 00:00:00

In many situations, sample data is obtained from a noisy or imperfect source.
In order to address such corruptions, this paper introduces the concept of a
sampling corrector. Such algorithms use structure that the distribution is
purported to have, in order to allow one to make "on-the-fly" corrections to
samples drawn from probability distributions. These algorithms then act as
filters between the noisy data and the end user.
  We show connections between sampling correctors, distribution learning
algorithms, and distribution property testing algorithms. We show that these
connections can be utilized to expand the applicability of known distribution
learning and property testing algorithms as well as to achieve improved
algorithms for those tasks.
  As a first step, we show how to design sampling correctors using proper
learning algorithms. We then focus on the question of whether algorithms for
sampling correctors can be more efficient in terms of sample complexity than
learning algorithms for the analogous families of distributions. When
correcting monotonicity, we show that this is indeed the case when also granted
query access to the cumulative distribution function. We also obtain sampling
correctors for monotonicity without this stronger type of access, provided that
the distribution be originally very close to monotone (namely, at a distance
$O(1/\log^2 n)$). In addition to that, we consider a restricted error model
that aims at capturing "missing data" corruptions. In this model, we show that
distributions that are close to monotone have sampling correctors that are
significantly more efficient than achievable by the learning approach.
  We also consider the question of whether an additional source of independent
random bits is required by sampling correctors to implement the correction
process.
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
Authors: Lingxiao He, Jian Liang, Haiqing Li, Zhenan Sun
Date: 2018-01-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Dictionary Learning, Person Re-Identification

Partial person re-identification (re-id) is a challenging problem, where only
several partial observations (images) of people are available for matching.
However, few studies have provided flexible solutions to identifying a person
in an image containing arbitrary part of the body. In this paper, we propose a
fast and accurate matching method to address this problem. The proposed method
leverages Fully Convolutional Network (FCN) to generate fix-sized spatial
feature maps such that pixel-level features are consistent. To match a pair of
person images of different sizes, a novel method called Deep Spatial feature
Reconstruction (DSR) is further developed to avoid explicit alignment.
Specifically, DSR exploits the reconstructing error from popular dictionary
learning models to calculate the similarity between different spatial feature
maps. In that way, we expect that the proposed FCN can decrease the similarity
of coupled images from different persons and increase that from the same
person. Experimental results on two partial person datasets demonstrate the
efficiency and effectiveness of the proposed method in comparison with several
state-of-the-art partial person re-id approaches. Additionally, DSR achieves
competitive results on a benchmark person dataset Market1501 with 83.58\%
Rank-1 accuracy.
On Unifying Deep Generative Models
Authors: Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing
Date: 2017-06-02 00:00:00
Proceeding: ICLR 2018 1

Deep generative models have achieved impressive success in recent years.
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as
emerging families for generative model learning, have largely been considered
as two distinct paradigms and received extensive independent studies
respectively. This paper aims to establish formal connections between GANs and
VAEs through a new formulation of them. We interpret sample generation in GANs
as performing posterior inference, and show that GANs and VAEs involve
minimizing KL divergences of respective posterior and inference distributions
with opposite directions, extending the two learning phases of classic
wake-sleep algorithm, respectively. The unified view provides a powerful tool
to analyze a diverse set of existing model variants, and enables to transfer
techniques across research lines in a principled way. For example, we apply the
importance weighting method in VAE literatures for improved GAN learning, and
enhance VAEs with an adversarial mechanism that leverages generated samples.
Experiments show generality and effectiveness of the transferred techniques.
SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data
Authors: Qi Chen, Weichao Qiu, Yi Zhang, Lingxi Xie, Alan Yuille
Date: 2018-04-01 00:00:00
Tasks: Classification, General Classification

State-of-the-art techniques of artificial intelligence, in particular deep
learning, are mostly data-driven. However, collecting and manually labeling a
large scale dataset is both difficult and expensive. A promising alternative is
to introduce synthesized training data, so that the dataset size can be
significantly enlarged with little human labor. But, this raises an important
problem in active vision: given an {\bf infinite} data space, how to
effectively sample a {\bf finite} subset to train a visual classifier? This
paper presents an approach for learning from synthesized data effectively. The
motivation is straightforward -- increasing the probability of seeing difficult
training data. We introduce a module named {\bf SampleAhead} to formulate the
learning process into an online communication between a {\em classifier} and a
{\em sampler}, and update them iteratively. In each round, we adjust the
sampling distribution according to the classification results, and train the
classifier using the data sampled from the updated distribution. Experiments
are performed by introducing synthesized images rendered from ShapeNet models
to assist PASCAL3D+ classification. Our approach enjoys higher classification
accuracy, especially in the scenario of a limited number of training samples.
This demonstrates its efficiency in exploring the infinite data space.
Supervised multiway factorization
Authors: Eric F. Lock, Gen Li
Date: 2016-09-11 00:00:00
Tasks: Dimensionality Reduction

We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway
(i.e., tensor) data that incorporates auxiliary covariates, SupCP. SupCP
generalizes the supervised singular value decomposition (SupSVD) for
vector-valued observations, to allow for observations that have the form of a
matrix or higher-order array. Such data are increasingly encountered in
biomedical research and other fields. We describe a likelihood-based latent
variable representation of the CP factorization, in which the latent variables
are informed by additional covariates. We give conditions for identifiability,
and develop an EM algorithm for simultaneous estimation of all model
parameters. SupCP can be used for dimension reduction, capturing latent
structures that are more accurate and interpretable due to covariate
supervision. Moreover, SupCP specifies a full probability distribution for a
multiway data observation with given covariate values, which can be used for
predictive modeling. We conduct comprehensive simulations to evaluate the SupCP
algorithm. We apply it to a facial image database with facial descriptors
(e.g., smiling / not smiling) as covariates, and to a study of amino acid
fluorescence. Software is available at https://github.com/lockEF/SupCP .
Modeling Individual Differences in Game Behavior using HMM
Authors: Sara Bunian, Alessandro Canossa, Randy Colvin, Magy Seif El-Nasr
Date: 2018-04-01 00:00:00

Player modeling is an important concept that has gained much attention in
game research due to its utility in developing adaptive techniques to target
better designs for engagement and retention. Previous work has explored
modeling individual differences using machine learning algorithms per- formed
on aggregated game actions. However, players' individual differences may be
better manifested through sequential patterns of the in-game player's actions.
While few works have explored sequential analysis of player data, none have
explored the use of Hidden Markov Models (HMM) to model individual differences,
which is the topic of this paper. In par- ticular, we developed a modeling
approach using data col- lected from players playing a Role-Playing Game (RPG).
Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of
player in-game behaviors to model individual differences, and 2. using the
output of the HMM, we generate behavioral features used to classify real world
players' characteristics, including game expertise and the big five personality
traits. Our results show predictive power for some of personality traits, such
as game expertise and conscientiousness, but the most influential factor was
game expertise.
The Structure Transfer Machine Theory and Applications
Authors: Baochang Zhang, Lian Zhuo, Ze Wang, Jungong Han, Xian-Tong Zhen
Date: 2018-04-01 00:00:00
Tasks: image-classification, Image Classification, Object Tracking, Representation Learning

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }.
Graph Correspondence Transfer for Person Re-identification
Authors: Qin Zhou, Heng Fan, Shibao Zheng, Hang Su, Xinzhe Li, Shuang Wu, Haibin Ling
Date: 2018-04-01 00:00:00
Tasks: Graph Matching, Person Re-Identification

In this paper, we propose a graph correspondence transfer (GCT) approach for
person re-identification. Unlike existing methods, the GCT model formulates
person re-identification as an off-line graph matching and on-line
correspondence transferring problem. In specific, during training, the GCT
model aims to learn off-line a set of correspondence templates from positive
training pairs with various pose-pair configurations via patch-wise graph
matching. During testing, for each pair of test samples, we select a few
training pairs with the most similar pose-pair configurations as references,
and transfer the correspondences of these references to test pair for feature
distance calculation. The matching score is derived by aggregating distances
from different references. For each probe image, the gallery image with the
highest matching score is the re-identifying result. Compared to existing
algorithms, our GCT can handle spatial misalignment caused by large variations
in view angles and human poses owing to the benefits of patch-wise graph
matching. Extensive experiments on five benchmarks including VIPeR, Road,
PRID450S, 3DPES and CUHK01 evidence the superior performance of GCT model over
other state-of-the-art methods.
Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks
Authors: Andreas Kölsch, Ashutosh Mishra, Saurabh Varshneya, Muhammad Zeshan Afzal, Marcus Liwicki
Date: 2018-04-01 00:00:00
Tasks: Data Augmentation, Semantic Segmentation

This paper introduces a very challenging dataset of historic German documents
and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate
handwritten annotations of any kind in these documents. The handwritten
annotations can appear in form of underlines and text by using various writing
instruments, e.g., the use of pencils makes the data more challenging. We train
and evaluate various end-to-end semantic segmentation approaches and report the
results. The task is to classify the pixels of documents into two classes:
background and handwritten annotation. The best model achieves a mean
Intersection over Union (IoU) score of 95.6% on the test documents of the
presented dataset. We also present a comparison of different strategies used
for data augmentation and training on our presented dataset. For evaluation, we
use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout
Analysis for Challenging Medieval Manuscripts.
Can Multisensory Cues in VR Help Train Pattern Recognition to Citizen Scientists?
Authors: Alina Striner
Date: 2018-04-01 00:00:00
Tasks: Transfer Learning

As the internet of things (IoT) has integrated physical and digital
technologies, designing for multiple sensory media (mulsemedia) has become more
attainable. Designing technology for multiple senses has the capacity to
improve virtual realism, extend our ability to process information, and more
easily transfer knowledge between physical and digital environments. HCI
researchers are beginning to explore the viability of integrating multimedia
into virtual experiences, however research has yet to consider whether
mulsemedia truly enhances realism, immersion and knowledge transfer. My work
developing StreamBED, a VR training platform to train citizen science water
monitors plans to consider the role of mulsemedia in immersion and learning
goals. Future findings about the role of mulsemedia in learning contexts will
potentially allow learners to experience, connect to, learn from spaces that
are impossible to experience firsthand.
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
Authors: Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini, Simon J. Thorpe, Timothée Masquelier
Date: 2018-03-31 00:00:00
Tasks: Diagnostic

The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain. Yet these networks are much less energy-efficient than their biological counterparts, and they are typically trained with backpropagation, which is extremely data-hungry. To address these limitations, we used a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme. We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lower layers and reward-modulated STDP (R-STDP) for the higher ones. In short, with R-STDP a correct (resp. incorrect) decision leads to STDP (resp. anti-STDP). This approach led to an accuracy of $97.2\%$ on MNIST, without requiring an external classifier. In addition, we demonstrated that R-STDP extracts features that are diagnostic for the task at hand, and discards the other ones, whereas STDP extracts any feature that repeats. Finally, our approach is biologically plausible, hardware friendly, and energy-efficient.
Real-world Anomaly Detection in Surveillance Videos
Authors: Waqas Sultani, Chen Chen, Mubarak Shah
Date: 2018-01-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Abnormal Event Detection In Video, Activity Recognition, Anomaly Detection, Anomaly Detection In Surveillance Videos, Multiple Instance Learning, Semi-supervised Anomaly Detection, Weakly-supervised Video Anomaly Detection

Surveillance videos are able to capture a variety of realistic anomalies. In
this paper, we propose to learn anomalies by exploiting both normal and
anomalous videos. To avoid annotating the anomalous segments or clips in
training videos, which is very time consuming, we propose to learn anomaly
through the deep multiple instance ranking framework by leveraging weakly
labeled training videos, i.e. the training labels (anomalous or normal) are at
video-level instead of clip-level. In our approach, we consider normal and
anomalous videos as bags and video segments as instances in multiple instance
learning (MIL), and automatically learn a deep anomaly ranking model that
predicts high anomaly scores for anomalous video segments. Furthermore, we
introduce sparsity and temporal smoothness constraints in the ranking loss
function to better localize anomaly during training. We also introduce a new
large-scale first of its kind dataset of 128 hours of videos. It consists of
1900 long and untrimmed real-world surveillance videos, with 13 realistic
anomalies such as fighting, road accident, burglary, robbery, etc. as well as
normal activities. This dataset can be used for two tasks. First, general
anomaly detection considering all anomalies in one group and all normal
activities in another group. Second, for recognizing each of 13 anomalous
activities. Our experimental results show that our MIL method for anomaly
detection achieves significant improvement on anomaly detection performance as
compared to the state-of-the-art approaches. We provide the results of several
recent deep learning baselines on anomalous activity recognition. The low
recognition performance of these baselines reveals that our dataset is very
challenging and opens more opportunities for future work. The dataset is
available at: https://webpages.uncc.edu/cchen62/dataset.html
Restoration of Pansharpened Images by Conditional Filtering in the PCA Domain
Authors: Joan Duran, Antoni Buades
Date: 2017-10-02 00:00:00
Tasks: Pansharpening

Pansharpening techniques aim at fusing low-resolution multispectral (MS)
images and high-resolution panchromatic (PAN) images to produce high-resolution
MS images. Despite significant progress in the field, spectral and spatial
distortions might still compromise the quality of the results. We introduce a
restoration strategy to mitigate artifacts of fused products. After applying
the Principal Component Analysis (PCA) transform to a pansharpened image, the
chromatic components are filtered conditionally to the geometry of PAN. The
structural component is then replaced by the locally histogram-matched PAN for
spatial enhancement. Experimental results illustrate the efficiency of the
proposed restoration chain.
HOUDINI: Lifelong Learning as Program Synthesis
Authors: Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
Date: 2018-03-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Lifelong learning, Program Synthesis, Transfer Learning

We present a neurosymbolic framework for the lifelong learning of algorithmic
tasks that mix perception and procedural reasoning. Reusing high-level concepts
across domains and learning complex procedures are key challenges in lifelong
learning. We show that a program synthesis approach that combines gradient
descent with combinatorial search over programs can be a more effective
response to these challenges than purely neural methods. Our framework, called
HOUDINI, represents neural networks as strongly typed, differentiable
functional programs that use symbolic higher-order combinators to compose a
library of neural functions. Our learning algorithm consists of: (1) a symbolic
program synthesizer that performs a type-directed search over parameterized
programs, and decides on the library functions to reuse, and the architectures
to combine them, while learning a sequence of tasks; and (2) a neural module
that trains these programs using stochastic gradient descent. We evaluate
HOUDINI on three benchmarks that combine perception with the algorithmic tasks
of counting, summing, and shortest-path computation. Our experiments show that
HOUDINI transfers high-level concepts more effectively than traditional
transfer learning and progressive neural networks, and that the typed
representation of networks significantly accelerates the search.
Fundamental Resource Trade-offs for Encoded Distributed Optimization
Authors: A. Salman Avestimehr, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi
Date: 2018-03-31 00:00:00
Tasks: Distributed Computing, Distributed Optimization

Dealing with the shear size and complexity of today's massive data sets
requires computational platforms that can analyze data in a parallelized and
distributed fashion. A major bottleneck that arises in such modern distributed
computing environments is that some of the worker nodes may run slow. These
nodes a.k.a.~stragglers can significantly slow down computation as the slowest
node may dictate the overall computational time. A recent computational
framework, called encoded optimization, creates redundancy in the data to
mitigate the effect of stragglers. In this paper we develop novel mathematical
understanding for this framework demonstrating its effectiveness in much
broader settings than was previously understood. We also analyze the
convergence behavior of iterative encoded optimization algorithms, allowing us
to characterize fundamental trade-offs between convergence rate, size of data
set, accuracy, computational load (or data redundancy), and straggler
toleration in this framework.