pwc-archive/papers-with-abstracts huggingface dataset:
CosFace: Large Margin Cosine Loss for Deep Face Recognition
Authors: Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, Wei Liu
Date: 2018-01-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Identification, Face Recognition, Face Verification

Face recognition has made extraordinary progress owing to the advancement of
deep convolutional neural networks (CNNs). The central task of face
recognition, including face verification and identification, involves face
feature discrimination. However, the traditional softmax loss of deep CNNs
usually lacks the power of discrimination. To address this problem, recently
several loss functions such as center loss, large margin softmax loss, and
angular softmax loss have been proposed. All these improved losses share the
same idea: maximizing inter-class variance and minimizing intra-class variance.
In this paper, we propose a novel loss function, namely large margin cosine
loss (LMCL), to realize this idea from a different perspective. More
specifically, we reformulate the softmax loss as a cosine loss by $L_2$
normalizing both features and weight vectors to remove radial variations, based
on which a cosine margin term is introduced to further maximize the decision
margin in the angular space. As a result, minimum intra-class variance and
maximum inter-class variance are achieved by virtue of normalization and cosine
decision margin maximization. We refer to our model trained with LMCL as
CosFace. Extensive experimental evaluations are conducted on the most popular
public-domain face recognition datasets such as MegaFace Challenge, Youtube
Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art
performance on these benchmarks, which confirms the effectiveness of our
proposed approach.
Towards whole-body CT Bone Segmentation
Authors: André Klein, Jan Warszawski, Jens Hillengaß, Klaus H. Maier-Hein
Date: 2018-04-03 00:00:00
Tasks: Medical Image Segmentation, Segmentation

Bone segmentation from CT images is a task that has been worked on for
decades. It is an important ingredient to several diagnostics or treatment
planning approaches and relevant to various diseases. As high-quality manual
and semi-automatic bone segmentation is very time-consuming, a reliable and
fully automatic approach would be of great interest in many scenarios. In this
publication, we propose a UNet inspired architecture to address the task using
Deep Learning. We evaluated the approach on whole-body CT scans of patients
suffering from multiple myeloma. As the disease decomposes the bone, an
accurate segmentation is of utmost importance for the evaluation of bone
density, disease staging and localization of focal lesions. The method was
evaluated on an in-house data-set of 6000 2D image slices taken from 15
whole-body CT scans, achieving a dice score of 0.96 and an IOU of 0.94.
Seeing Voices and Hearing Faces: Cross-modal biometric matching
Authors: Arsha Nagrani, Samuel Albanie, Andrew Zisserman
Date: 2018-04-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Recognition, Speaker Identification

We introduce a seemingly impossible task: given only an audio clip of someone
speaking, decide which of two face images is the speaker. In this paper we
study this, and a number of related cross-modal tasks, aimed at answering the
question: how much can we infer from the voice about the face and vice versa?
We study this task "in the wild", employing the datasets that are now publicly
available for face recognition from static images (VGGFace) and speaker
identification from audio (VoxCeleb). These provide training and testing
scenarios for both static and dynamic testing of cross-modal matching. We make
the following contributions: (i) we introduce CNN architectures for both binary
and multi-way cross-modal face and audio matching, (ii) we compare dynamic
testing (where video information is available, but the audio is not from the
same video) with static testing (where only a single still image is available),
and (iii) we use human testing as a baseline to calibrate the difficulty of the
task. We show that a CNN can indeed be trained to solve this task in both the
static and dynamic scenarios, and is even well above chance on 10-way
classification of the face given the voice. The CNN matches human performance
on easy examples (e.g. different gender across faces) but exceeds human
performance on more challenging examples (e.g. faces with the same gender, age
and nationality).
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Authors: Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, Jiri Matas
Date: 2017-11-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deblurring, object-detection, Object Detection

We present DeblurGAN, an end-to-end learned method for motion deblurring. The
learning is based on a conditional GAN and the content loss . DeblurGAN
achieves state-of-the art performance both in the structural similarity measure
and visual appearance. The quality of the deblurring model is also evaluated in
a novel way on a real-world problem -- object detection on (de-)blurred images.
The method is 5 times faster than the closest competitor -- DeepDeblur. We also
introduce a novel method for generating synthetic motion blurred images from
sharp ones, allowing realistic dataset augmentation.
  The model, code and the dataset are available at
https://github.com/KupynOrest/DeblurGAN
When will you do what? - Anticipating Temporal Occurrences of Activities
Authors: Yazan Abu Farha, Alexander Richard, Juergen Gall
Date: 2018-04-03 00:00:00
Proceeding: CVPR 2018 6

Analyzing human actions in videos has gained increased attention recently.
While most works focus on classifying and labeling observed video frames or
anticipating the very recent future, making long-term predictions over more
than just a few seconds is a task with many practical applications that has not
yet been addressed. In this paper, we propose two methods to predict a
considerably large amount of future actions and their durations. Both, a CNN
and an RNN are trained to learn future video labels based on previously seen
content. We show that our methods generate accurate predictions of the future
even for long videos with a huge amount of different actions and can even deal
with noisy or erroneous input information.
Hyperspherical Variational Auto-Encoders
Authors: Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak
Date: 2018-04-03 00:00:00
Tasks: Link Prediction

The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure. To address this issue we propose using a von Mises-Fisher (vMF) distribution instead, leading to a hyperspherical latent space. Through a series of experiments we show how such a hyperspherical VAE, or $\mathcal{S}$-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, $\mathcal{N}$-VAE, in low dimensions on other data types. Code at http://github.com/nicola-decao/s-vae-tf and https://github.com/nicola-decao/s-vae-pytorch
Security Consideration For Deep Learning-Based Image Forensics
Authors: Wei Zhao, Pengpeng Yang, Rongrong Ni, Yao Zhao, Haorui Wu
Date: 2018-03-29 00:00:00
Tasks: Deep Learning, Image Forensics

Recently, image forensics community has paied attention to the research on
the design of effective algorithms based on deep learning technology and facts
proved that combining the domain knowledge of image forensics and deep learning
would achieve more robust and better performance than the traditional schemes.
Instead of improving it, in this paper, the safety of deep learning based
methods in the field of image forensics is taken into account. To the best of
our knowledge, this is a first work focusing on this topic. Specifically, we
experimentally find that the method using deep learning would fail when adding
the slight noise into the images (adversarial images). Furthermore, two kinds
of strategys are proposed to enforce security of deep learning-based method.
Firstly, an extra penalty term to the loss function is added, which is referred
to the 2-norm of the gradient of the loss with respect to the input images, and
then an novel training method are adopt to train the model by fusing the normal
and adversarial images. Experimental results show that the proposed algorithm
can achieve good performance even in the case of adversarial images and provide
a safety consideration for deep learning-based image forensics
Learning to Guide Decoding for Image Captioning
Authors: Wenhao Jiang, Lin Ma, Xinpeng Chen, Hanwang Zhang, Wei Liu
Date: 2018-04-03 00:00:00
Tasks: Attribute, Decoder, Image Captioning

Recently, much advance has been made in image captioning, and an
encoder-decoder framework has achieved outstanding performance for this task.
In this paper, we propose an extension of the encoder-decoder framework by
adding a component called guiding network. The guiding network models the
attribute properties of input images, and its output is leveraged to compose
the input of the decoder at each time step. The guiding network can be plugged
into the current encoder-decoder framework and trained in an end-to-end manner.
Hence, the guiding vector can be adaptively learned according to the signal
from the decoder, making itself to embed information from both image and
language. Additionally, discriminative supervision can be employed to further
improve the quality of guidance. The advantages of our proposed approach are
verified by experiments carried out on the MS COCO dataset.
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
Authors: Nian Liu, Junwei Han, Ming-Hsuan Yang
Date: 2017-08-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: RGB Salient Object Detection, Saliency Detection

Contexts play an important role in the saliency detection task. However,
given a context region, not all contextual information is helpful for the final
task. In this paper, we propose a novel pixel-wise contextual attention
network, i.e., the PiCANet, to learn to selectively attend to informative
context locations for each pixel. Specifically, for each pixel, it can generate
an attention map in which each attention weight corresponds to the contextual
relevance at each context location. An attended contextual feature can then be
constructed by selectively aggregating the contextual information. We formulate
the proposed PiCANet in both global and local forms to attend to global and
local contexts, respectively. Both models are fully differentiable and can be
embedded into CNNs for joint training. We also incorporate the proposed models
with the U-Net architecture to detect salient objects. Extensive experiments
show that the proposed PiCANets can consistently improve saliency detection
performance. The global and local PiCANets facilitate learning global contrast
and homogeneousness, respectively. As a result, our saliency model can detect
salient objects more accurately and uniformly, thus performing favorably
against the state-of-the-art methods.
PhaseNet for Video Frame Interpolation
Authors: Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus Gross, Christopher Schroers
Date: 2018-04-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Deep Learning, Video Frame Interpolation

Most approaches for video frame interpolation require accurate dense
correspondences to synthesize an in-between frame. Therefore, they do not
perform well in challenging scenarios with e.g. lighting changes or motion
blur. Recent deep learning approaches that rely on kernels to represent motion
can only alleviate these problems to some extent. In those cases, methods that
use a per-pixel phase-based motion representation have been shown to work well.
However, they are only applicable for a limited amount of motion. We propose a
new approach, PhaseNet, that is designed to robustly handle challenging
scenarios while also coping with larger motion. Our approach consists of a
neural network decoder that directly estimates the phase decomposition of the
intermediate frame. We show that this is superior to the hand-crafted
heuristics previously used in phase-based methods and also compares favorably
to recent deep learning based approaches for video frame interpolation on
challenging datasets.
Weakly Supervised Instance Segmentation using Class Peak Response
Authors: Yanzhao Zhou, Yi Zhu, Qixiang Ye, Qiang Qiu, Jianbin Jiao
Date: 2018-04-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Image-level Supervised Instance Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation, Weakly-supervised instance segmentation

Weakly supervised instance segmentation with image-level labels, instead of
expensive pixel-level masks, remains unexplored. In this paper, we tackle this
challenging problem by exploiting class peak responses to enable a
classification network for instance mask extraction. With image labels
supervision only, CNN classifiers in a fully convolutional manner can produce
class response maps, which specify classification confidence at each image
location. We observed that local maximums, i.e., peaks, in a class response map
typically correspond to strong visual cues residing inside each instance.
Motivated by this, we first design a process to stimulate peaks to emerge from
a class response map. The emerged peaks are then back-propagated and
effectively mapped to highly informative regions of each object instance, such
as instance boundaries. We refer to the above maps generated from class peak
responses as Peak Response Maps (PRMs). PRMs provide a fine-detailed
instance-level representation, which allows instance masks to be extracted even
with some off-the-shelf methods. To the best of our knowledge, we for the first
time report results for the challenging image-level supervised instance
segmentation task. Extensive experiments show that our method also boosts
weakly supervised pointwise localization as well as semantic segmentation
performance, and reports state-of-the-art results on popular benchmarks,
including PASCAL VOC 2012 and MS COCO.
EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning
Authors: Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber, Sven Magg, Stefan Wermter
Date: 2018-04-03 00:00:00
Tasks: Classification, Deep Reinforcement Learning, Emotion Classification, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Acoustically expressed emotions can make communication with a robot more
efficient. Detecting emotions like anger could provide a clue for the robot
indicating unsafe/undesired situations. Recently, several deep neural
network-based models have been proposed which establish new state-of-the-art
results in affective state evaluation. These models typically start processing
at the end of each utterance, which not only requires a mechanism to detect the
end of an utterance but also makes it difficult to use them in a real-time
communication scenario, e.g. human-robot interaction. We propose the EmoRL
model that triggers an emotion classification as soon as it gains enough
confidence while listening to a person speaking. As a result, we minimize the
need for segmenting the audio signal for classification and achieve lower
latency as the audio signal is processed incrementally. The method is
competitive with the accuracy of a strong baseline model, while allowing much
earlier prediction.
On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
Authors: Patrick Glauner, Radu State, Petko Valtchev, Diogo Duarte
Date: 2018-01-17 00:00:00
Tasks: BIG-bench Machine Learning

In machine learning, a bias occurs whenever training sets are not
representative for the test data, which results in unreliable models. The most
common biases in data are arguably class imbalance and covariate shift. In this
work, we aim to shed light on this topic in order to increase the overall
attention to this issue in the field of machine learning. We propose a scalable
novel framework for reducing multiple biases in high-dimensional data sets in
order to train more reliable predictors. We apply our methodology to the
detection of irregular power usage from real, noisy industrial data. In
emerging markets, irregular power usage, and electricity theft in particular,
may range up to 40% of the total electricity distributed. Biased data sets are
of particular issue in this domain. We show that reducing these biases
increases the accuracy of the trained predictors. Our models have the potential
to generate significant economic value in a real world application, as they are
being deployed in a commercial software for the detection of irregular power
usage.
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
Authors: Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama
Date: 2017-05-19 00:00:00
Tasks: Variational Inference

Analyzing the underlying structure of multiple time-sequences provides
insights into the understanding of social networks and human activities. In
this work, we present the \emph{Bayesian nonparametric Poisson process
allocation} (BaNPPA), a latent-function model for time-sequences, which
automatically infers the number of latent functions. We model the intensity of
each sequence as an infinite mixture of latent functions, each of which is
obtained using a function drawn from a Gaussian process. We show that a
technical challenge for the inference of such mixture models is the
unidentifiability of the weights of the latent functions. We propose to cope
with the issue by regulating the volume of each latent function within a
variational inference algorithm. Our algorithm is computationally efficient and
scales well to large data sets. We demonstrate the usefulness of our proposed
model through experiments on both synthetic and real-world data sets.
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
Authors: Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison
Date: 2018-04-03 00:00:00

The representation of geometry in real-time 3D perception systems continues
to be a critical research issue. Dense maps capture complete surface shape and
can be augmented with semantic labels, but their high dimensionality makes them
computationally costly to store and process, and unsuitable for rigorous
probabilistic inference. Sparse feature-based representations avoid these
problems, but capture only partial scene information and are mainly useful for
localisation only.
  We present a new compact but dense representation of scene geometry which is
conditioned on the intensity data from a single image and generated from a code
consisting of a small number of parameters. We are inspired by work both on
learned depth from images, and auto-encoders. Our approach is suitable for use
in a keyframe-based monocular dense SLAM system: While each keyframe with a
code can produce a depth map, the code can be optimised efficiently jointly
with pose variables and together with the codes of overlapping keyframes to
attain global consistency. Conditioning the depth map on the image allows the
code to only represent aspects of the local geometry which cannot directly be
predicted from the image. We explain how to learn our code representation, and
demonstrate its advantageous properties in monocular SLAM.
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
Authors: Jiale Cao, Yanwei Pang, Xuelong. Li
Date: 2018-04-03 00:00:00
Tasks: object-detection, Object Detection, Pedestrian Detection

To better detect pedestrians of various scales, deep multi-scale methods
usually detect pedestrians of different scales by different in-network layers.
However, the semantic levels of features from different layers are usually
inconsistent. In this paper, we propose a multi-branch and high-level semantic
network by gradually splitting a base network into multiple different branches.
As a result, the different branches have the same depth and the output features
of different branches have similarly high-level semantics. Due to the
difference of receptive fields, the different branches are suitable to detect
pedestrians of different scales. Meanwhile, the multi-branch network does not
introduce additional parameters by sharing convolutional weights of different
branches. To further improve detection performance, skip-layer connections
among different branches are used to add context to the branch of relatively
small receptive filed, and dilated convolution is incorporated into part
branches to enlarge the resolutions of output feature maps. When they are
embedded into Faster RCNN architecture, the weighted scores of proposal
generation network and proposal classification network are further proposed.
Experiments on KITTI dataset, Caltech pedestrian dataset, and Citypersons
dataset demonstrate the effectiveness of proposed method. On these pedestrian
datasets, the proposed method achieves state-of-the-art detection performance.
Moreover, experiments on COCO benchmark show the proposed method is also
suitable for general object detection.
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Authors: Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, Tatsuya Harada
Date: 2017-12-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, image-classification, Image Classification, Multi-Source Unsupervised Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

In this work, we present a method for unsupervised domain adaptation. Many
adversarial learning methods train domain classifier networks to distinguish
the features as either a source or target and train a feature generator network
to mimic the discriminator. Two problems exist with these methods. First, the
domain classifier only tries to distinguish the features as a source or target
and thus does not consider task-specific decision boundaries between classes.
Therefore, a trained generator can generate ambiguous features near class
boundaries. Second, these methods aim to completely match the feature
distributions between different domains, which is difficult because of each
domain's characteristics.
  To solve these problems, we introduce a new approach that attempts to align
distributions of source and target by utilizing the task-specific decision
boundaries. We propose to maximize the discrepancy between two classifiers'
outputs to detect target samples that are far from the support of the source. A
feature generator learns to generate target features near the support to
minimize the discrepancy. Our method outperforms other methods on several
datasets of image classification and semantic segmentation. The codes are
available at \url{https://github.com/mil-tokyo/MCD_DA}