pwc-archive/papers-with-abstracts huggingface dataset:
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
Authors: Lei Zhang, Shan-Shan Wang, Guang-Bin Huang, WangMeng Zuo, Jian Yang, David Zhang
Date: 2019-03-25 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

In many practical transfer learning scenarios, the feature distribution is
different across the source and target domains (i.e. non-i.i.d.). Maximum mean
discrepancy (MMD), as a domain discrepancy metric, has achieved promising
performance in unsupervised domain adaptation (DA). We argue that MMD-based DA
methods ignore the data locality structure, which, to some extent, would cause
the negative transfer effect. The locality plays an important role in
minimizing the nonlinear local domain discrepancy underlying the marginal
distributions. For better exploiting the domain locality, a novel local
generative discrepancy metric (LGDM) based intermediate domain generation
learning called Manifold Criterion guided Transfer Learning (MCTL) is proposed
in this paper. The merits of the proposed MCTL are four-fold: 1) the concept of
manifold criterion (MC) is first proposed as a measure validating the
distribution matching across domains, and domain adaptation is achieved if the
MC is satisfied; 2) the proposed MC can well guide the generation of the
intermediate domain sharing similar distribution with the target domain, by
minimizing the local domain discrepancy; 3) a global generative discrepancy
metric (GGDM) is presented, such that both the global and local discrepancy can
be effectively and positively reduced; 4) a simplified version of MCTL called
MCTL-S is presented under a perfect domain generation assumption for more
generic learning scenario. Experiments on a number of benchmark visual transfer
tasks demonstrate the superiority of the proposed manifold criterion guided
generative transfer method, by comparing with other state-of-the-art methods.
The source code is available in https://github.com/wangshanshanCQU/MCTL.
Deep Shape from Polarization
Authors: Yunhao Ba, Alex Ross Gilbert, Franklin Wang, Jinfa Yang, Rui Chen, Yiqin Wang, Lei Yan, Boxin Shi, Achuta Kadambi
Date: 2019-03-25 00:00:00
Proceeding: ECCV 2020 8

This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning. The previous state-of-the-art methods for SfP have been purely physics-based. We see value in these principled models, and blend these physical models as priors into a neural network architecture. This proposed approach achieves results that exceed the previous state-of-the-art on a challenging dataset we introduce. This dataset consists of polarization images taken over a range of object textures, paints, and lighting conditions. We report that our proposed method achieves the lowest test error on each tested condition in our dataset, showing the value of blending data-driven and physics-driven approaches.
Accurate Global Trajectory Alignment using Poles and Road Markings
Authors: Haohao Hu, Marc Sons, Christoph Stiller
Date: 2019-03-25 00:00:00

Currently, digital maps are indispensable for automated driving. However, due
to the low precision and reliability of GNSS particularly in urban areas,
fusing trajectories of independent recording sessions and different regions is
a challenging task. To bypass the flaws from direct incorporation of GNSS
measurements for geo-referencing, the usage of aerial imagery seems promising.
Furthermore, more accurate geo-referencing improves the global map accuracy and
allows to estimate the sensor calibration error. In this paper, we present a
novel geo-referencing approach to align trajectories to aerial imagery using
poles and road markings. To match extracted features from sensor observations
to aerial imagery landmarks robustly, a RANSAC-based matching approach is
applied in a sliding window. For that, we assume that the trajectories are
roughly referenced to the imagery which can be achieved by rough GNSS
measurements from a low-cost GNSS receiver. Finally, we align the initial
trajectories precisely to the aerial imagery by minimizing a geometric cost
function comprising all determined matches. Evaluations performed on data
recorded in Karlsruhe, Germany show that our algorithm yields trajectories
which are accurately referenced to the used aerial imagery.
Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
Authors: Chaoyou Fu, Xiang Wu, Yibo Hu, Huaibo Huang, Ran He
Date: 2019-03-25 00:00:00
Tasks: Face Recognition, Heterogeneous Face Recognition

Heterogeneous Face Recognition (HFR) is a challenging issue because of the large domain discrepancy and a lack of heterogeneous data. This paper considers HFR as a dual generation problem, and proposes a novel Dual Variational Generation (DVG) framework. It generates large-scale new paired heterogeneous images with the same identity from noise, for the sake of reducing the domain gap of HFR. Specifically, we first introduce a dual variational autoencoder to represent a joint distribution of paired heterogeneous images. Then, in order to ensure the identity consistency of the generated paired heterogeneous images, we impose a distribution alignment in the latent space and a pairwise identity preserving in the image space. Moreover, the HFR network reduces the domain discrepancy by constraining the pairwise feature distances between the generated paired heterogeneous images. Extensive experiments on four HFR databases show that our method can significantly improve state-of-the-art results. The related code is available at https://github.com/BradyFU/DVG.
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
Authors: Amanda Duarte, Francisco Roldan, Miquel Tubau, Janna Escur, Santiago Pascual, Amaia Salvador, Eva Mohedano, Kevin McGuinness, Jordi Torres, Xavier Giro-i-Nieto
Date: 2019-03-25 00:00:00
Tasks: Face Generation, Generative Adversarial Network

Speech is a rich biometric signal that contains information about the
identity, gender and emotional state of the speaker. In this work, we explore
its potential to generate face images of a speaker by conditioning a Generative
Adversarial Network (GAN) with raw speech input. We propose a deep neural
network that is trained from scratch in an end-to-end fashion, generating a
face directly from the raw speech waveform without any additional identity
information (e.g reference image or one-hot encoding). Our model is trained in
a self-supervised approach by exploiting the audio and visual signals naturally
aligned in videos. With the purpose of training from video data, we present a
novel dataset collected for this work, with high-quality videos of youtubers
with notable expressiveness in both the speech and visual signals.
Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
Authors: Christoph Benzmüller, Xavier Parent, Leendert van der Torre
Date: 2019-03-25 00:00:00
Tasks: Legal Reasoning

A framework and methodology---termed LogiKEy---for the design and engineering of ethical reasoners, normative theories and deontic logics is presented. The overall motivation is the development of suitable means for the control and governance of intelligent autonomous systems. LogiKEy's unifying formal framework is based on semantical embeddings of deontic logics, logic combinations and ethico-legal domain theories in expressive classic higher-order logic (HOL). This meta-logical approach enables the provision of powerful tool support in LogiKEy: off-the-shelf theorem provers and model finders for HOL are assisting the LogiKEy designer of ethical intelligent agents to flexibly experiment with underlying logics and their combinations, with ethico-legal domain theories, and with concrete examples---all at the same time. Continuous improvements of these off-the-shelf provers, without further ado, leverage the reasoning performance in LogiKEy. Case studies, in which the LogiKEy framework and methodology has been applied and tested, give evidence that HOL's undecidability often does not hinder efficient experimentation.
DeepRED: Deep Image Prior Powered by RED
Authors: Gary Mataev, Michael Elad, Peyman Milanfar
Date: 2019-03-25 00:00:00
Tasks: Deblurring, Denoising, Image Super-Resolution

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DIP and RED) can be merged into a highly effective unsupervised recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested problems.
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
Authors: Yinlong Liu, Xuechen Li, Manning Wang, Guang Chen, Zhijian Song, Alois Knoll
Date: 2019-03-25 00:00:00
Tasks: parameter estimation, Pose Estimation, Translation

Absolute pose estimation is a fundamental problem in computer vision, and it
is a typical parameter estimation problem, meaning that efforts to solve it
will always suffer from outlier-contaminated data. Conventionally, for a fixed
dimensionality d and the number of measurements N, a robust estimation problem
cannot be solved faster than O(N^d). Furthermore, it is almost impossible to
remove d from the exponent of the runtime of a globally optimal algorithm.
However, absolute pose estimation is a geometric parameter estimation problem,
and thus has special constraints. In this paper, we consider pairwise
constraints and propose a globally optimal algorithm for solving the absolute
pose estimation problem. The proposed algorithm has a linear complexity in the
number of correspondences at a given outlier ratio. Concretely, we first
decouple the rotation and the translation subproblems by utilizing the pairwise
constraints, and then we solve the rotation subproblem using the
branch-and-bound algorithm. Lastly, we estimate the translation based on the
known rotation by using another branch-and-bound algorithm. The advantages of
our method are demonstrated via thorough testing on both synthetic and
real-world data
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
Authors: Mason Liu, Menglong Zhu, Marie White, Yinxiao Li, Dmitry Kalenichenko
Date: 2019-03-25 00:00:00
Tasks: object-detection, Object Detection, Object Recognition, Real-Time Object Detection, Video Object Detection

Models and examples built with TensorFlow
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Authors: Kangxue Yin, Zhiqin Chen, Hui Huang, Daniel Cohen-Or, Hao Zhang
Date: 2019-03-25 00:00:00
Tasks: Generative Adversarial Network, Translation

We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and chairs, while there is neither a pairing between shapes from the domains as supervision nor any point-wise correspondence between any shapes. Once trained, LOGAN takes a shape from one domain and transforms it into the other. Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes concatenate multi-scale shape features, resulting in an overcomplete representation. The translator is based on a generative adversarial network (GAN), operating in the latent space, where an adversarial loss enforces cross-domain translation while a feature preservation loss ensures that the right shape features are preserved for a natural shape transform. We conduct ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. We show that LOGAN is able to learn what shape features to preserve during shape translation, either local or non-local, whether content or style, depending solely on the input domains for training.
Efficient Bird Eye View Proposals for 3D Siamese Tracking
Authors: Jesus Zarzar, Silvio Giancola, Bernard Ghanem
Date: 2019-03-25 00:00:00
Tasks: Object Tracking, Region Proposal

Tracking vehicles in LIDAR point clouds is a challenging task due to the sparsity of the data and the dense search space. The lack of structure in point clouds impedes the use of convolution filters usually employed in 2D object tracking. In addition, structuring point clouds is cumbersome and implies losing fine-grained information. As a result, generating proposals in 3D space is expensive and inefficient. In this paper, we leverage the dense and structured Bird Eye View (BEV) representation of LIDAR point clouds to efficiently search for objects of interest. We use an efficient Region Proposal Network and generate a small number of object proposals in 3D. Successively, we refine our selection of 3D object candidates by exploiting the similarity capability of a 3D Siamese network. We regularize the latter 3D Siamese network for shape completion to enhance its discrimination capability. Our method attempts to solve both for an efficient search space in the BEV space and a meaningful selection using 3D LIDAR point cloud. We show that the Region Proposal in the BEV outperforms Bayesian methods such as Kalman and Particle Filters in providing proposal by a significant margin and that such candidates are suitable for the 3D Siamese network. By training our method end-to-end, we outperform the previous baseline in vehicle tracking by 12% / 18% in Success and Precision when using only 16 candidates.
Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring
Authors: Dongwon Park, Jisoo Kim, Se Young Chun
Date: 2019-03-25 00:00:00
Tasks: Deblurring, Image Deblurring, Single Image Deblurring, Video Deblurring

Multi-scale approach has been used for blind image / video deblurring
problems to yield excellent performance for both conventional and recent
deep-learning-based state-of-the-art methods. Bicubic down-sampling is a
typical choice for multi-scale approach to reduce spatial dimension after
filtering with a fixed kernel. However, this fixed kernel may be sub-optimal
since it may destroy important information for reliable deblurring such as
strong edges. We propose convolutional neural network (CNN)-based down-scale
methods for multi-scale deep-learning-based non-uniform single image
deblurring. We argue that our CNN-based down-scaling effectively reduces the
spatial dimension of the original image, while learned kernels with multiple
channels may well-preserve necessary details for deblurring tasks. For each
scale, we adopt to use RCAN (Residual Channel Attention Networks) as a backbone
network to further improve performance. Our proposed method yielded
state-of-the-art performance on GoPro dataset by large margin. Our proposed
method was able to achieve 2.59dB higher PSNR than the current state-of-the-art
method by Tao. Our proposed CNN-based down-scaling was the key factor for this
excellent performance since the performance of our network without it was
decreased by 1.98dB. The same networks trained with GoPro set were also
evaluated on large-scale Su dataset and our proposed method yielded 1.15dB
better PSNR than the Tao's method. Qualitative comparisons on Lai dataset also
confirmed the superior performance of our proposed method over other
state-of-the-art methods.
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
Authors: Zhenpeng Chen, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, Xuanzhe Liu
Date: 2018-06-07 00:00:00
Tasks: Classification, Cross-Lingual Sentiment Classification, General Classification, Machine Translation, Representation Learning, Sentiment Analysis, Sentiment Classification

Sentiment classification typically relies on a large amount of labeled data.
In practice, the availability of labels is highly imbalanced among different
languages, e.g., more English texts are labeled than texts in any other
languages, which creates a considerable inequality in the quality of related
information services received by users speaking different languages. To tackle
this problem, cross-lingual sentiment classification approaches aim to transfer
knowledge learned from one language that has abundant labeled examples (i.e.,
the source language, usually English) to another language with fewer labels
(i.e., the target language). The source and the target languages are usually
bridged through off-the-shelf machine translation tools. Through such a
channel, cross-language sentiment patterns can be successfully learned from
English and transferred into the target languages. This approach, however,
often fails to capture sentiment knowledge specific to the target language, and
thus compromises the accuracy of the downstream classification task. In this
paper, we employ emojis, which are widely available in many languages, as a new
channel to learn both the cross-language and the language-specific sentiment
patterns. We propose a novel representation learning method that uses emoji
prediction as an instrument to learn respective sentiment-aware representations
for each language. The learned representations are then integrated to
facilitate cross-lingual sentiment classification. The proposed method
demonstrates state-of-the-art performance on benchmark datasets, which is
sustained even when sentiment labels are scarce.
DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image
Authors: Pengfei Yao, Zheng Fang, Fan Wu, Yao Feng, Jiwei Li
Date: 2019-03-25 00:00:00
Tasks: Decoder

Recovering 3D human body shape and pose from 2D images is a challenging task
due to high complexity and flexibility of human body, and relatively less 3D
labeled data. Previous methods addressing these issues typically rely on
predicting intermediate results such as body part segmentation, 2D/3D joints,
silhouette mask to decompose the problem into multiple sub-tasks in order to
utilize more 2D labels. Most previous works incorporated parametric body shape
model in their methods and predict parameters in low-dimensional space to
represent human body. In this paper, we propose to directly regress the 3D
human mesh from a single color image using Convolutional Neural Network(CNN).
We use an efficient representation of 3D human shape and pose which can be
predicted through an encoder-decoder neural network. The proposed method
achieves state-of-the-art performance on several 3D human body datasets
including Human3.6M, SURREAL and UP-3D with even faster running speed.
SAC-Net: Spatial Attenuation Context for Salient Object Detection
Authors: Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Tianyu Wang, Pheng-Ann Heng
Date: 2019-03-25 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects. Our key idea is to adaptively propagate and aggregate the image context features with variable attenuation over the entire feature maps. To achieve this, we design the spatial attenuation context (SAC) module to recurrently translate and aggregate the context features independently with different attenuation factors and then to attentively learn the weights to adaptively integrate the aggregated context features. By further embedding the module to process individual layers in a deep network, namely SAC-Net, we can train the network end-to-end and optimize the context features for detecting salient objects. Compared with 29 state-of-the-art methods, experimental results show that our method performs favorably over all the others on six common benchmark data, both quantitatively and visually.
Enhanced Transfer Learning with ImageNet Trained Classification Layer
Authors: Tasfia Shermin, Shyh Wei Teng, Manzur Murshed, Guojun Lu, Ferdous Sohel, Manoranjan Paul
Date: 2019-03-25 00:00:00
Tasks: Classification, Domain Adaptation, General Classification, Transfer Learning

Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained source network are transferred to the target network followed by fine-tuning. Prior research has shown that this approach is capable of improving task performance. However, the impact of the ImageNet pre-trained classification layer in parameter fine-tuning is mostly unexplored in the literature. In this paper, we propose a fine-tuning approach with the pre-trained classification layer. We employ layer-wise fine-tuning to determine which layers should be frozen for optimal performance. Our empirical analysis demonstrates that the proposed fine-tuning performs better than traditional fine-tuning. This finding indicates that the pre-trained classification layer holds less category-specific or more global information than believed earlier. Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task. Our study manifests that careful normalization and scaling are essential for creating harmony between the pre-trained and new layers for target domain adaptation. We evaluate the proposed depth augmented networks for fine-tuning on several challenging benchmark datasets and show that they can achieve higher classification accuracy than contemporary transfer learning approaches.
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Authors: Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin
Date: 2019-03-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder, Language Modeling, Language Modelling, Response Generation, Scheduling

Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. The VAE objective consists of two terms, (i) reconstruction and (ii) KL regularization, balanced by a weighting hyper-parameter \beta. One notorious training difficulty is that the KL term tends to vanish. In this paper we study scheduling schemes for \beta, and show that KL vanishing is caused by the lack of good latent codes in training the decoder at the beginning of optimization. To remedy this, we propose a cyclical annealing schedule, which repeats the process of increasing \beta multiple times. This new procedure allows the progressive learning of more meaningful latent codes, by leveraging the informative representations of previous cycles as warm re-starts. The effectiveness of cyclical annealing is validated on a broad range of NLP tasks, including language modeling, dialog response generation and unsupervised language pre-training.