pwc-archive/papers-with-abstracts huggingface dataset:
What Will I Do Next? The Intention from Motion Experiment
Authors: Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio, Vittorio Murino
Date: 2017-08-03 00:00:00
Tasks: Early  Classification, General Classification, Prediction

In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal embedded in an action sequence. Since the same action can be performed
with different intentions, this problem is more challenging but yet affordable
as proved by quantitative cognitive studies which exploit the 3D kinematics
acquired through motion capture systems. In this paper, we bridge cognitive and
computer vision studies, by demonstrating the effectiveness of video-based
approaches for the prediction of human intentions. Precisely, we propose
Intention from Motion, a new paradigm where, without using any contextual
information, we consider instantaneous grasping motor acts involving a bottle
in order to forecast why the bottle itself has been reached (to pass it or to
place in a box, or to pour or to drink the liquid inside). We process only the
grasping onsets casting intention prediction as a classification framework.
Leveraging on our multimodal acquisition (3D motion capture data and 2D optical
videos), we compare the most commonly used 3D descriptors from cognitive
studies with state-of-the-art video-based techniques. Since the two analyses
achieve an equivalent performance, we demonstrate that computer vision tools
are effective in capturing the kinematics and facing the cognitive problem of
human intention prediction.
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Authors: Kai Kang, Hongsheng Li, Junjie Yan, Xingyu Zeng, Bin Yang, Tong Xiao, Cong Zhang, Zhe Wang, Ruohui Wang, Xiaogang Wang, Wanli Ouyang
Date: 2016-04-09 00:00:00
Tasks: Novel Object Detection, Object, object-detection, Object Detection, Object Recognition

The state-of-the-art performance for object detection has been significantly
improved over the past two years. Besides the introduction of powerful deep
neural networks such as GoogleNet and VGG, novel object detection frameworks
such as R-CNN and its successors, Fast R-CNN and Faster R-CNN, play an
essential role in improving the state-of-the-art. Despite their effectiveness
on still images, those frameworks are not specifically designed for object
detection from videos. Temporal and contextual information of videos are not
fully investigated and utilized. In this work, we propose a deep learning
framework that incorporates temporal and contextual information from tubelets
obtained in videos, which dramatically improves the baseline performance of
existing still-image detection frameworks when they are applied to videos. It
is called T-CNN, i.e. tubelets with convolutional neueral networks. The
proposed framework won the recently introduced object-detection-from-video
(VID) task with provided data in the ImageNet Large-Scale Visual Recognition
Challenge 2015 (ILSVRC2015).
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Authors: Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Date: 2017-08-03 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Human action recognition from skeletal data is a hot research topic and
important in many open domain applications of computer vision, thanks to
recently introduced 3D sensors. In the literature, naive methods simply
transfer off-the-shelf techniques from video to the skeletal representation.
However, the current state-of-the-art is contended between to different
paradigms: kernel-based methods and feature learning with (recurrent) neural
networks. Both approaches show strong performances, yet they exhibit heavy, but
complementary, drawbacks. Motivated by this fact, our work aims at combining
together the best of the two paradigms, by proposing an approach where a
shallow network is fed with a covariance representation. Our intuition is that,
as long as the dynamics is effectively modeled, there is no need for the
classification network to be deep nor recurrent in order to score favorably. We
validate this hypothesis in a broad experimental analysis over 6 publicly
available datasets.
CRF Autoencoder for Unsupervised Dependency Parsing
Authors: Jiong Cai, Yong Jiang, Kewei Tu
Date: 2017-08-03 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Grammar Induction, Unsupervised Dependency Parsing

Unsupervised dependency parsing, which tries to discover linguistic
dependency structures from unannotated data, is a very challenging task. Almost
all previous work on this task focuses on learning generative models. In this
paper, we develop an unsupervised dependency parsing model based on the CRF
autoencoder. The encoder part of our model is discriminative and globally
normalized which allows us to use rich features as well as universal linguistic
priors. We propose an exact algorithm for parsing as well as a tractable
learning algorithm. We evaluated the performance of our model on eight
multilingual treebanks and found that our model achieved comparable performance
with state-of-the-art approaches.
Sensor Transformation Attention Networks
Authors: Stefan Braun, Daniel Neil, Enea Ceolini, Jithendar Anumula, Shih-Chii Liu
Date: 2017-08-03 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder

Recent work on encoder-decoder models for sequence-to-sequence mapping has
shown that integrating both temporal and spatial attention mechanisms into
neural networks increases the performance of the system substantially. In this
work, we report on the application of an attentional signal not on temporal and
spatial regions of the input, but instead as a method of switching among inputs
themselves. We evaluate the particular role of attentional switching in the
presence of dynamic noise in the sensors, and demonstrate how the attentional
signal responds dynamically to changing noise levels in the environment to
achieve increased performance on both audio and visual tasks in three
commonly-used datasets: TIDIGITS, Wall Street Journal, and GRID. Moreover, the
proposed sensor transformation network architecture naturally introduces a
number of advantages that merit exploration, including ease of adding new
sensors to existing architectures, attentional interpretability, and increased
robustness in a variety of noisy environments not seen during training.
Finally, we demonstrate that the sensor selection attention mechanism of a
model trained only on the small TIDIGITS dataset can be transferred directly to
a pre-existing larger network trained on the Wall Street Journal dataset,
maintaining functionality of switching between sensors to yield a dramatic
reduction of error in the presence of noise.
Curriculum Dropout
Authors: Pietro Morerio, Jacopo Cavazza, Riccardo Volpi, Rene Vidal, Vittorio Murino
Date: 2017-03-18 00:00:00
Proceeding: ICCV 2017 10
Tasks: image-classification, Image Classification, Scheduling

Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages
over-specific co-adaptations of feature detectors, preventing overfitting and
improving network generalization. Besides, Dropout can be interpreted as an
approximate model aggregation technique, where an exponential number of smaller
networks are averaged in order to get a more powerful ensemble. In this paper,
we show that using a fixed dropout probability during training is a suboptimal
choice. We thus propose a time scheduling for the probability of retaining
neurons in the network. This induces an adaptive regularization scheme that
smoothly increases the difficulty of the optimization problem. This idea of
"starting easy" and adaptively increasing the difficulty of the learning
problem has its roots in curriculum learning and allows one to train better
models. Indeed, we prove that our optimization strategy implements a very
general curriculum scheme, by gradually adding noise to both the input and
intermediate feature representations within the network architecture.
Experiments on seven image classification datasets and different network
architectures show that our method, named Curriculum Dropout, frequently yields
to better generalization and, at worst, performs just as well as the standard
Dropout method.
Revisiting Activation Regularization for Language RNNs
Authors: Stephen Merity, Bryan McCann, Richard Socher
Date: 2017-08-03 00:00:00
Tasks: L2 Regularization, Language Modeling, Language Modelling

Recurrent neural networks (RNNs) serve as a fundamental building block for
many sequence tasks across natural language processing. Recent research has
focused on recurrent dropout techniques or custom RNN cells in order to improve
performance. Both of these can require substantial modifications to the machine
learning model or to the underlying RNN configurations. We revisit traditional
regularization techniques, specifically L2 regularization on RNN activations
and slowness regularization over successive hidden states, to improve the
performance of RNNs on the task of language modeling. Both of these techniques
require minimal modification to existing RNN architectures and result in
performance improvements comparable or superior to more complicated
regularization techniques or custom cell architectures. These regularization
techniques can be used without any modification on optimized LSTM
implementations such as the NVIDIA cuDNN LSTM.
Beyond Low Rank: A Data-Adaptive Tensor Completion Method
Authors: Lei Zhang, Wei Wei, Qinfeng Shi, Chunhua Shen, Anton Van Den Hengel, Yanning Zhang
Date: 2017-08-03 00:00:00

Low rank tensor representation underpins much of recent progress in tensor
completion. In real applications, however, this approach is confronted with two
challenging problems, namely (1) tensor rank determination; (2) handling real
tensor data which only approximately fulfils the low-rank requirement. To
address these two issues, we develop a data-adaptive tensor completion model
which explicitly represents both the low-rank and non-low-rank structures in a
latent tensor. Representing the non-low-rank structure separately from the
low-rank one allows priors which capture the important distinctions between the
two, thus enabling more accurate modelling, and ultimately, completion. Through
defining a new tensor rank, we develop a sparsity induced prior for the
low-rank structure, with which the tensor rank can be automatically determined.
The prior for the non-low-rank structure is established based on a mixture of
Gaussians which is shown to be flexible enough, and powerful enough, to inform
the completion process for a variety of real tensor data. With these two
priors, we develop a Bayesian minimum mean squared error estimate (MMSE)
framework for inference which provides the posterior mean of missing entries as
well as their uncertainty. Compared with the state-of-the-art methods in
various applications, the proposed model produces more accurate completion
results.
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
Authors: Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, Hang Su
Date: 2017-08-03 00:00:00
Tasks: Quantization

Low-bit deep neural networks (DNNs) become critical for embedded applications
due to their low storage requirement and computing efficiency. However, they
suffer much from the non-negligible accuracy drop. This paper proposes the
stochastic quantization (SQ) algorithm for learning accurate low-bit DNNs. The
motivation is due to the following observation. Existing training algorithms
approximate the real-valued elements/filters with low-bit representation all
together in each iteration. The quantization errors may be small for some
elements/filters, while are remarkable for others, which lead to inappropriate
gradient direction during training, and thus bring notable accuracy drop.
Instead, SQ quantizes a portion of elements/filters to low-bit with a
stochastic probability inversely proportional to the quantization error, while
keeping the other portion unchanged with full-precision. The quantized and
full-precision portions are updated with corresponding gradients separately in
each iteration. The SQ ratio is gradually increased until the whole network is
quantized. This procedure can greatly compensate the quantization error and
thus yield better accuracy for low-bit DNNs. Experiments show that SQ can
consistently and significantly improve the accuracy for different low-bit DNNs
on various datasets and various network structures.
Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images
Authors: Wei-Lin Hsiao, Kristen Grauman
Date: 2017-07-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Topic Models

What defines a visual style? Fashion styles emerge organically from how
people assemble outfits of clothing, making them difficult to pin down with a
computational model. Low-level visual similarity can be too specific to detect
stylistically similar images, while manually crafted style categories can be
too abstract to capture subtle style differences. We propose an unsupervised
approach to learn a style-coherent representation. Our method leverages
probabilistic polylingual topic models based on visual attributes to discover a
set of latent style factors. Given a collection of unlabeled fashion images,
our approach mines for the latent styles, then summarizes outfits by how they
mix those styles. Our approach can organize galleries of outfits by style
without requiring any style labels. Experiments on over 100K images demonstrate
its promise for retrieving, mixing, and summarizing fashion images by their
style.
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems
Authors: Saishankar Katri Pulliyakode, Sheetal Kalyani
Date: 2017-08-03 00:00:00
Tasks: Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Wireless systems perform rate adaptation to transmit at highest possible
instantaneous rates. Rate adaptation has been increasingly granular over
generations of wireless systems. The base-station uses SINR and packet decode
feedback called acknowledgement/no acknowledgement (ACK/NACK) to perform rate
adaptation. SINR is used for rate anchoring called inner look adaptation and
ACK/NACK is used for fine offset adjustments called Outer Loop Link Adaptation
(OLLA). We cast the OLLA as a reinforcement learning problem of the class of
Multi-Armed Bandits (MAB) where the different offset values are the arms of the
bandit. In OLLA, as the offset values increase, the probability of packet error
also increase, and every user equipment (UE) has a desired Block Error Rate
(BLER) to meet certain Quality of Service (QoS) requirements. For this MAB we
propose a binary search based algorithm which achieves a Probably Approximately
Correct (PAC) solution making use of bounds from large deviation theory and
confidence bounds. In addition to this we also discuss how a Thompson sampling
or UCB based method will not help us meet the target objectives. Finally,
simulation results are provided on an LTE system simulator and thereby prove
the efficacy of our proposed algorithm.
Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning
Authors: Jan Niehues, Eunah Cho
Date: 2017-08-03 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Multi-Task Learning, NMT, POS, Translation

Linguistic resources such as part-of-speech (POS) tags have been extensively
used in statistical machine translation (SMT) frameworks and have yielded
better performances. However, usage of such linguistic annotations in neural
machine translation (NMT) systems has been left under-explored.
  In this work, we show that multi-task learning is a successful and a easy
approach to introduce an additional knowledge into an end-to-end neural
attentional model. By jointly training several natural language processing
(NLP) tasks in one system, we are able to leverage common information and
improve the performance of the individual task.
  We analyze the impact of three design decisions in multi-task learning: the
tasks used in training, the training schedule, and the degree of parameter
sharing across the tasks, which is defined by the network architecture. The
experiments are conducted for an German to English translation task. As
additional linguistic resources, we exploit POS information and named-entities
(NE). Experiments show that the translation quality can be improved by up to
1.5 BLEU points under the low-resource condition. The performance of the POS
tagger is also improved using the multi-task learning scheme.
Dual Motion GAN for Future-Flow Embedded Video Prediction
Authors: Xiaodan Liang, Lisa Lee, Wei Dai, Eric P. Xing
Date: 2017-08-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Prediction, Representation Learning, Video Prediction

Future frame prediction in videos is a promising avenue for unsupervised
video representation learning. Video frames are naturally generated by the
inherent pixel flows from preceding frames based on the appearance and motion
dynamics in the video. However, existing methods focus on directly
hallucinating pixel values, resulting in blurry predictions. In this paper, we
develop a dual motion Generative Adversarial Net (GAN) architecture, which
learns to explicitly enforce future-frame predictions to be consistent with the
pixel-wise flows in the video through a dual-learning mechanism. The primal
future-frame prediction and dual future-flow prediction form a closed loop,
generating informative feedback signals to each other for better video
prediction. To make both synthesized future frames and flows indistinguishable
from reality, a dual adversarial training method is proposed to ensure that the
future-flow prediction is able to help infer realistic future-frames, while the
future-frame prediction in turn leads to realistic optical flows. Our dual
motion GAN also handles natural motion uncertainty in different pixel locations
with a new probabilistic motion encoder, which is based on variational
autoencoders. Extensive experiments demonstrate that the proposed dual motion
GAN significantly outperforms state-of-the-art approaches on synthesizing new
video frames and predicting future flows. Our model generalizes well across
diverse visual scenes and shows superiority in unsupervised video
representation learning.
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
Authors: Aliasghar Mortazi, Jeremy Burt, Ulas Bagci
Date: 2017-08-03 00:00:00
Tasks: Heart Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Non-invasive detection of cardiovascular disorders from radiology scans
requires quantitative image analysis of the heart and its substructures. There
are well-established measurements that radiologists use for diseases assessment
such as ejection fraction, volume of four chambers, and myocardium mass. These
measurements are derived as outcomes of precise segmentation of the heart and
its substructures. The aim of this paper is to provide such measurements
through an accurate image segmentation algorithm that automatically delineates
seven substructures of the heart from MRI and/or CT scans. Our proposed method
is based on multi-planar deep convolutional neural networks (CNN) with an
adaptive fusion strategy where we automatically utilize complementary
information from different planes of the 3D scans for improved delineations.
For CT and MRI, we have separately designed three CNNs (the same architectural
configuration) for three planes, and have trained the networks from scratch for
voxel-wise labeling for the following cardiac structures: myocardium of left
ventricle (Myo), left atrium (LA), left ventricle (LV), right atrium (RA),
right ventricle (RV), ascending aorta (Ao), and main pulmonary artery (PA). We
have evaluated the proposed method with 4-fold-cross validation on the
multi-modality whole heart segmentation challenge (MM-WHS 2017) dataset. The
precision and dice index of 0.93 and 0.90, and 0.87 and 0.85 were achieved for
CT and MR images, respectively. While a CT volume was segmented about 50
seconds, an MRI scan was segmented around 17 seconds with the GPUs/CUDA
implementation.
ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image Processing
Authors: Zhenqiang Ying, Ge Li, Sixin Wen, Guozhen Tan
Date: 2017-08-03 00:00:00

Single materials have colors which form straight lines in RGB space. However,
in severe shadow cases, those lines do not intersect the origin, which is
inconsistent with the description of most literature. This paper is concerned
with the detection and correction of the offset between the intersection and
origin. First, we analyze the reason for forming that offset via an optical
imaging model. Second, we present a simple and effective way to detect and
remove the offset. The resulting images, named ORGB, have almost the same
appearance as the original RGB images while are more illumination-robust for
color space conversion. Besides, image processing using ORGB instead of RGB is
free from the interference of shadows. Finally, the proposed offset correction
method is applied to road detection task, improving the performance both in
quantitative and qualitative evaluations.
Attention Transfer from Web Images for Video Recognition
Authors: Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Date: 2017-08-03 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Video Recognition

Training deep learning based video classifiers for action recognition
requires a large amount of labeled videos. The labeling process is
labor-intensive and time-consuming. On the other hand, large amount of
weakly-labeled images are uploaded to the Internet by users everyday. To
harness the rich and highly diverse set of Web images, a scalable approach is
to crawl these images to train deep learning based classifier, such as
Convolutional Neural Networks (CNN). However, due to the domain shift problem,
the performance of Web images trained deep classifiers tend to degrade when
directly deployed to videos. One way to address this problem is to fine-tune
the trained models on videos, but sufficient amount of annotated videos are
still required. In this work, we propose a novel approach to transfer knowledge
from image domain to video domain. The proposed method can adapt to the target
domain (i.e. video data) with limited amount of training data. Our method maps
the video frames into a low-dimensional feature space using the
class-discriminative spatial attention map for CNNs. We design a novel Siamese
EnergyNet structure to learn energy functions on the attention maps by jointly
optimizing two loss functions, such that the attention map corresponding to a
ground truth concept would have higher energy. We conduct extensive experiments
on two challenging video recognition datasets (i.e. TVHI and UCF101), and
demonstrate the efficacy of our proposed method.