pwc-archive/papers-with-abstracts huggingface dataset:
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
Authors: Joris Guérin, Olivier Gibaru, Stéphane Thiery, Eric Nyiri
Date: 2017-03-22 00:00:00
Tasks: Clustering

This paper describes a method for clustering data that are spread out over
large regions and which dimensions are on different scales of measurement. Such
an algorithm was developed to implement a robotics application consisting in
sorting and storing objects in an unsupervised way. The toy dataset used to
validate such application consists of Lego bricks of different shapes and
colors. The uncontrolled lighting conditions together with the use of RGB color
features, respectively involve data with a large spread and different levels of
measurement between data dimensions. To overcome the combination of these two
characteristics in the data, we have developed a new weighted K-means
algorithm, called gap-ratio K-means, which consists in weighting each dimension
of the feature space before running the K-means algorithm. The weight
associated with a feature is proportional to the ratio of the biggest gap
between two consecutive data points, and the average of all the other gaps.
This method is compared with two other variants of K-means on the Lego bricks
clustering problem as well as two other common classification datasets.
Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review
Authors: Manuela Hirschmugl, Heinz Gallaun, Matthias Dees, Pawan Datta, Janik Deutscher, Nikos Koutsias, Mathias Schardt
Date: 2017-01-10 00:00:00
Tasks: Change Detection, Earth Observation, General Classification, Time Series, Time Series Analysis, Time Series Classification

Purpose of review: This paper presents a review of the current state of the
art in remote sensing based monitoring of forest disturbances and forest
degradation from optical Earth Observation data. Part one comprises an overview
of currently available optical remote sensing sensors, which can be used for
forest disturbance and degradation mapping. Part two reviews the two main
categories of existing approaches: classical image-to-image change detection
and time series analysis. Recent findings: With the launch of the Sentinel-2a
satellite and available Landsat imagery, time series analysis has become the
most promising but also most demanding category of degradation mapping
approaches. Four time series classification methods are distinguished. The
methods are explained and their benefits and drawbacks are discussed. A
separate chapter presents a number of recent forest degradation mapping studies
for two different ecosystems: temperate forests with a geographical focus on
Europe and tropical forests with a geographical focus on Africa. Summary: The
review revealed that a wide variety of methods for the detection of forest
degradation is already available. Today, the main challenge is to transfer
these approaches to high resolution time series data from multiple sensors.
Future research should also focus on the classification of disturbance types
and the development of robust up-scalable methods to enable near real time
disturbance mapping in support of operational reactive measures.
An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning
Authors: Fan Wu, Zhongwen Xu, Yi Yang
Date: 2017-03-22 00:00:00
Tasks: Deep Reinforcement Learning, Object, Referring Expression, Reinforcement Learning, Retrieval

We propose an end-to-end approach to the natural language object retrieval
task, which localizes an object within an image according to a natural language
description, i.e., referring expression. Previous works divide this problem
into two independent stages: first, compute region proposals from the image
without the exploration of the language description; second, score the object
proposals with regard to the referring expression and choose the top-ranked
proposals. The object proposals are generated independently from the referring
expression, which makes the proposal generation redundant and even irrelevant
to the referred object. In this work, we train an agent with deep reinforcement
learning, which learns to move and reshape a bounding box to localize the
object according to the referring expression. We incorporate both the spatial
and temporal context information into the training procedure. By simultaneously
exploiting local visual information, the spatial and temporal context and the
referring language a priori, the agent selects an appropriate action to take at
each time. A special action is defined to indicate when the agent finds the
referred object, and terminate the procedure. We evaluate our model on various
datasets, and our algorithm significantly outperforms the compared algorithms.
Notably, the accuracy improvement of our method over the recent method GroundeR
and SCRC on the ReferItGame dataset are 7.67% and 18.25%, respectively.
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
Authors: Florian Chabot, Mohamed Chaouch, Jaonary Rabarisoa, Céline Teulière, Thierry Chateau
Date: 2017-03-22 00:00:00
Proceeding: CVPR 2017 7
Tasks: Pose Estimation, vehicle detection, Vehicle Pose Estimation

In this paper, we present a novel approach, called Deep MANTA (Deep
Many-Tasks), for many-task vehicle analysis from a given image. A robust
convolutional network is introduced for simultaneous vehicle detection, part
localization, visibility characterization and 3D dimension estimation. Its
architecture is based on a new coarse-to-fine object proposal that boosts the
vehicle detection. Moreover, the Deep MANTA network is able to localize vehicle
parts even if these parts are not visible. In the inference, the network's
outputs are used by a real time robust pose estimation algorithm for fine
orientation estimation and 3D vehicle localization. We show in experiments that
our method outperforms monocular state-of-the-art approaches on vehicle
detection, orientation and 3D location tasks on the very challenging KITTI
benchmark.
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Authors: Giorgio Patrini, Alessandro Rozza, Aditya Menon, Richard Nock, Lizhen Qu
Date: 2016-09-13 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Image Classification, Learning with noisy labels, Noise Estimation

We present a theoretically grounded approach to train deep neural networks,
including recurrent networks, subject to class-dependent label noise. We
propose two procedures for loss correction that are agnostic to both
application domain and network architecture. They simply amount to at most a
matrix inversion and multiplication, provided that we know the probability of
each class being corrupted into another. We further show how one can estimate
these probabilities, adapting a recent technique for noise estimation to the
multi-class setting, and thus providing an end-to-end framework. Extensive
experiments on MNIST, IMDB, CIFAR-10, CIFAR-100 and a large scale dataset of
clothing images employing a diversity of architectures --- stacking dense,
convolutional, pooling, dropout, batch normalization, word embedding, LSTM and
residual layers --- demonstrate the noise robustness of our proposals.
Incidentally, we also prove that, when ReLU is the only non-linearity, the loss
curvature is immune to class-dependent label noise.
PathTrack: Fast Trajectory Annotation with Path Supervision
Authors: Santiago Manen, Michael Gygli, Dengxin Dai, Luc van Gool
Date: 2017-03-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Multiple Object Tracking, Object, Object Recognition, Object Tracking

Progress in Multiple Object Tracking (MOT) has been historically limited by
the size of the available datasets. We present an efficient framework to
annotate trajectories and use it to produce a MOT dataset of unprecedented
size. In our novel path supervision the annotator loosely follows the object
with the cursor while watching the video, providing a path annotation for each
object in the sequence. Our approach is able to turn such weak annotations into
dense box trajectories. Our experiments on existing datasets prove that our
framework produces more accurate annotations than the state of the art, in a
fraction of the time. We further validate our approach by crowdsourcing the
PathTrack dataset, with more than 15,000 person trajectories in 720 sequences.
Tracking approaches can benefit training on such large-scale datasets, as did
object recognition. We prove this by re-training an off-the-shelf person
matching network, originally trained on the MOT15 dataset, almost halving the
misclassification rate. Additionally, training on our data consistently
improves tracking results, both on our dataset and on MOT15. On the latter, we
improve the top-performing tracker (NOMT) dropping the number of IDSwitches by
18% and fragments by 5%.
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes
Authors: Guo-Jun Qi, Wei Liu, Charu Aggarwal, Thomas Huang
Date: 2017-03-22 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, text-classification, Text Classification, Zero-Shot Learning

In this paper, we present a label transfer model from texts to images for
image classification tasks. The problem of image classification is often much
more challenging than text classification. On one hand, labeled text data is
more widely available than the labeled images for classification tasks. On the
other hand, text data tends to have natural semantic interpretability, and they
are often more directly related to class labels. On the contrary, the image
features are not directly related to concepts inherent in class labels. One of
our goals in this paper is to develop a model for revealing the functional
relationships between text and image features as to directly transfer
intermodal and intramodal labels to annotate the images. This is implemented by
learning a transfer function as a bridge to propagate the labels between two
multimodal spaces. However, the intermodal label transfers could be undermined
by blindly transferring the labels of noisy texts to annotate images. To
mitigate this problem, we present an intramodal label transfer process, which
complements the intermodal label transfer by transferring the image labels
instead when relevant text is absent from the source corpus. In addition, we
generalize the inter-modal label transfer to zero-shot learning scenario where
there are only text examples available to label unseen classes of images
without any positive image examples. We evaluate our algorithm on an image
classification task and show the effectiveness with respect to the other
compared algorithms.
Video Frame Interpolation via Adaptive Convolution
Authors: Simon Niklaus, Long Mai, Feng Liu
Date: 2017-03-22 00:00:00
Proceeding: CVPR 2017 7
Tasks: Motion Estimation, Optical Flow Estimation, Video Frame Interpolation

Video frame interpolation typically involves two steps: motion estimation and
pixel synthesis. Such a two-step approach heavily depends on the quality of
motion estimation. This paper presents a robust video frame interpolation
method that combines these two steps into a single process. Specifically, our
method considers pixel synthesis for the interpolated frame as local
convolution over two input frames. The convolution kernel captures both the
local motion between the input frames and the coefficients for pixel synthesis.
Our method employs a deep fully convolutional neural network to estimate a
spatially-adaptive convolution kernel for each pixel. This deep neural network
can be directly trained end to end using widely available video data without
any difficult-to-obtain ground-truth data like optical flow. Our experiments
show that the formulation of video interpolation as a single convolution
process allows our method to gracefully handle challenges like occlusion, blur,
and abrupt brightness change and enables high-quality video frame
interpolation.
LogitBoost autoregressive networks
Authors: Marc Goessling
Date: 2017-03-22 00:00:00

Multivariate binary distributions can be decomposed into products of
univariate conditional distributions. Recently popular approaches have modeled
these conditionals through neural networks with sophisticated weight-sharing
structures. It is shown that state-of-the-art performance on several standard
benchmark datasets can actually be achieved by training separate probability
estimators for each dimension. In that case, model training can be trivially
parallelized over data dimensions. On the other hand, complexity control has to
be performed for each learned conditional distribution. Three possible methods
are considered and experimentally compared. The estimator that is employed for
each conditional is LogitBoost. Similarities and differences between the
proposed approach and autoregressive models based on neural networks are
discussed in detail.
CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural Networks
Authors: Xushen Han, Dajiang Zhou, Shihao Wang, Shinji Kimura
Date: 2017-03-22 00:00:00

Large-scale deep convolutional neural networks (CNNs) are widely used in
machine learning applications. While CNNs involve huge complexity, VLSI (ASIC
and FPGA) chips that deliver high-density integration of computational
resources are regarded as a promising platform for CNN's implementation. At
massive parallelism of computational units, however, the external memory
bandwidth, which is constrained by the pin count of the VLSI chip, becomes the
system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of
the flexibility to be reconfigured for the various parameters of CNNs. This
paper presents CNN-MERP to address these issues. CNN-MERP incorporates an
efficient memory hierarchy that significantly reduces the bandwidth
requirements from multiple optimizations including on/off-chip data allocation,
data flow optimization and data reuse. The proposed 2-level reconfigurability
is utilized to enable fast and efficient reconfiguration, which is based on the
control logic and the multiboot feature of FPGA. As a result, an external
memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower
than prior arts. Under limited DRAM bandwidth, a system throughput of
1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times
higher than the state-of-the-art FPGA implementations.
Knowledge Transfer for Melanoma Screening with Deep Learning
Authors: Afonso Menegola, Michel Fornaciali, Ramon Pires, Flávia Vasques Bittencourt, Sandra Avila, Eduardo Valle
Date: 2017-03-22 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, Medical Image Analysis, Skin Cancer Classification, Transfer Learning

Knowledge transfer impacts the performance of deep learning -- the state of
the art for image classification tasks, including automated melanoma screening.
Deep learning's greed for large amounts of training data poses a challenge for
medical tasks, which we can alleviate by recycling knowledge from models
trained on different tasks, in a scheme called transfer learning. Although much
of the best art on automated melanoma screening employs some form of transfer
learning, a systematic evaluation was missing. Here we investigate the presence
of transfer, from which task the transfer is sourced, and the application of
fine tuning (i.e., retraining of the deep learning model after transfer). We
also test the impact of picking deeper (and more expensive) models. Our results
favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an
AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.
Episode-Based Active Learning with Bayesian Neural Networks
Authors: Feras Dayoub, Niko Sünderhauf, Peter Corke
Date: 2017-03-21 00:00:00
Tasks: Active Learning

We investigate different strategies for active learning with Bayesian deep
neural networks. We focus our analysis on scenarios where new, unlabeled data
is obtained episodically, such as commonly encountered in mobile robotics
applications. An evaluation of different strategies for acquisition, updating,
and final training on the CIFAR-10 dataset shows that incremental network
updates with final training on the accumulated acquisition set are essential
for best performance, while limiting the amount of required human labeling
labor.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Authors: Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra
Date: 2016-10-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: General Classification, Image Attribution, image-classification, Image Classification, Interpretability Techniques for Deep Learning, Interpretable Machine Learning, Reinforcement Learning, Visual Question Answering, Visual Question Answering (VQA)

We propose a technique for producing "visual explanations" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce a coarse localization map highlighting important regions in the image for predicting the concept. Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers, (2) CNNs used for structured outputs, (3) CNNs used in tasks with multimodal inputs or reinforcement learning, without any architectural changes or re-training. We combine Grad-CAM with fine-grained visualizations to create a high-resolution class-discriminative visualization and apply it to off-the-shelf image classification, captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into their failure modes, (b) are robust to adversarial images, (c) outperform previous methods on localization, (d) are more faithful to the underlying model and (e) help achieve generalization by identifying dataset bias. For captioning and VQA, we show that even non-attention based models can localize inputs. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM helps users establish appropriate trust in predictions from models and show that Grad-CAM helps untrained users successfully discern a 'stronger' nodel from a 'weaker' one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/, along with a demo at http://gradcam.cloudcv.org, and a video at youtu.be/COjUB9Izk6E.
RobustFill: Neural Program Learning under Noisy I/O
Authors: Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli
Date: 2017-03-21 00:00:00
Proceeding: ICML 2017 8
Tasks: Program induction, Program Synthesis

The problem of automatically generating a computer program from some
specification has been studied since the early days of AI. Recently, two
competing approaches for automatic program learning have received significant
attention: (1) neural program synthesis, where a neural network is conditioned
on input/output (I/O) examples and learns to generate a program, and (2) neural
program induction, where a neural network generates new outputs directly using
a latent program representation.
  Here, for the first time, we directly compare both approaches on a
large-scale, real-world learning task. We additionally contrast to rule-based
program synthesis, which uses hand-crafted semantics to guide the program
generation. Our neural models use a modified attention RNN to allow encoding of
variable-sized sets of I/O pairs. Our best synthesis model achieves 92%
accuracy on a real-world test set, compared to the 34% accuracy of the previous
best neural synthesis approach. The synthesis model also outperforms a
comparable induction model on this task, but we more importantly demonstrate
that the strength of each approach is highly dependent on the evaluation metric
and end-user application. Finally, we show that we can train our neural models
to remain very robust to the type of noise expected in real-world data (e.g.,
typos), while a highly-engineered rule-based system fails entirely.
Learning Background-Aware Correlation Filters for Visual Tracking
Authors: Hamed Kiani Galoogahi, Ashton Fagg, Simon Lucey
Date: 2017-03-14 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Video Object Tracking, Visual Tracking

Correlation Filters (CFs) have recently demonstrated excellent performance in
terms of rapidly tracking objects under challenging photometric and geometric
variations. The strength of the approach comes from its ability to efficiently
learn - "on the fly" - how the object is changing over time. A fundamental
drawback to CFs, however, is that the background of the object is not be
modelled over time which can result in suboptimal results. In this paper we
propose a Background-Aware CF that can model how both the foreground and
background of the object varies over time. Our approach, like conventional CFs,
is extremely computationally efficient - and extensive experiments over
multiple tracking benchmarks demonstrate the superior accuracy and real-time
performance of our method compared to the state-of-the-art trackers including
those based on a deep learning paradigm.
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
Authors: Hamed Kiani Galoogahi, Ashton Fagg, Chen Huang, Deva Ramanan, Simon Lucey
Date: 2017-03-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object Tracking, Visual Object Tracking

In this paper, we propose the first higher frame rate video dataset (called
Need for Speed - NfS) and benchmark for visual object tracking. The dataset
consists of 100 videos (380K frames) captured with now commonly available
higher frame rate (240 FPS) cameras from real world scenarios. All frames are
annotated with axis aligned bounding boxes and all sequences are manually
labelled with nine visual attributes - such as occlusion, fast motion,
background clutter, etc. Our benchmark provides an extensive evaluation of many
recent and state-of-the-art trackers on higher frame rate sequences. We ranked
each of these trackers according to their tracking accuracy and real-time
performance. One of our surprising conclusions is that at higher frame rates,
simple trackers such as correlation filters outperform complex methods based on
deep networks. This suggests that for practical applications (such as in
robotics or embedded vision), one needs to carefully tradeoff bandwidth
constraints associated with higher frame rate acquisition, computational costs
of real-time analysis, and the required application accuracy. Our dataset and
benchmark allows for the first time (to our knowledge) systematic exploration
of such issues, and will be made available to allow for further research in
this space.