pwc-archive/papers-with-abstracts huggingface dataset:
Predicting the Sentiment Polarity and Rating of Yelp Reviews
Authors: Andrew Elkouri
Date: 2015-12-20 00:00:00
Tasks: Classification, regression

Online reviews of businesses have become increasingly important in recent
years, as customers and even competitors use them to judge the quality of a
business. Yelp is one of the most popular websites for users to write such
reviews, and it would be useful for them to be able to predict the sentiment or
even the star rating of a review. In this paper, we develop two classifiers to
perform positive/negative classification and 5-star classification. We use
Naive Bayes, Support Vector Machines, and Logistic Regression as models, and
achieved the best accuracy with Logistic Regression: 92.90% for
positive/negative classification, and 63.92% for 5-star classification. These
results demonstrate the quality of the Logistic Regression model using only the
text of the review, yet there is a promising opportunity for improvement with
more data, more features, and perhaps different models.
A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
Authors: Qiang Cui, Shu Wu, Yan Huang, Liang Wang
Date: 2017-11-14 00:00:00
Tasks: Sequential Recommendation

Sequential recommendation is one of fundamental tasks for Web applications.
Previous methods are mostly based on Markov chains with a strong Markov
assumption. Recently, recurrent neural networks (RNNs) are getting more and
more popular and has demonstrated its effectiveness in many tasks. The last
hidden state is usually applied as the sequence's representation to make
recommendation. Benefit from the natural characteristics of RNN, the hidden
state is a combination of long-term dependency and short-term interest to some
degrees. However, the monotonic temporal dependency of RNN impairs the user's
short-term interest. Consequently, the hidden state is not sufficient to
reflect the user's final interest. In this work, to deal with this problem, we
propose a Hierarchical Contextual Attention-based GRU (HCA-GRU) network. The
first level of HCA-GRU is conducted on the input. We construct a contextual
input by using several recent inputs based on the attention mechanism. This can
model the complicated correlations among recent items and strengthen the hidden
state. The second level is executed on the hidden state. We fuse the current
hidden state and a contextual hidden state built by the attention mechanism,
which leads to a more suitable user's overall interest. Experiments on two
real-world datasets show that HCA-GRU can effectively generate the personalized
ranking list and achieve significant improvement.
Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation
Authors: Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Zhixu Li, Jiajie Xu, Victor S. Sheng
Date: 2018-06-18 00:00:00
Tasks: Sequential Recommendation

Next Point-of-Interest (POI) recommendation is of great value for both
location-based service providers and users. Recently Recurrent Neural Networks
(RNNs) have been proved to be effective on sequential recommendation tasks.
However, existing RNN solutions rarely consider the spatio-temporal intervals
between neighbor check-ins, which are essential for modeling user check-in
behaviors in next POI recommendation. In this paper, we propose a new variant
of LSTM, named STLSTM, which implements time gates and distance gates into LSTM
to capture the spatio-temporal relation between successive check-ins.
Specifically, one-time gate and one distance gate are designed to control
short-term interest update, and another time gate and distance gate are
designed to control long-term interest update. Furthermore, to reduce the
number of parameters and improve efficiency, we further integrate coupled input
and forget gates with our proposed model. Finally, we evaluate the proposed
model using four real-world datasets from various location-based social
networks. Our experimental results show that our model significantly
outperforms the state-of-the-art approaches for next POI recommendation.
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
Authors: Sebastian Raschka
Date: 2018-11-13 00:00:00
Tasks: BIG-bench Machine Learning, model, Model Selection

The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages of each technique with references to theoretical and empirical studies. Further, recommendations are given to encourage best yet feasible practices in research and applications of machine learning. Common methods such as the holdout method for model evaluation and selection are covered, which are not recommended when working with small datasets. Different flavors of the bootstrap technique are introduced for estimating the uncertainty of performance estimates, as an alternative to confidence intervals via normal approximation if bootstrapping is computationally feasible. Common cross-validation techniques such as leave-one-out cross-validation and k-fold cross-validation are reviewed, the bias-variance trade-off for choosing k is discussed, and practical tips for the optimal choice of k are given based on empirical evidence. Different statistical tests for algorithm comparisons are presented, and strategies for dealing with multiple comparisons such as omnibus tests and multiple-comparison corrections are discussed. Finally, alternative methods for algorithm selection, such as the combined F-test 5x2 cross-validation and nested cross-validation, are recommended for comparing machine learning algorithms when datasets are small.
Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery
Authors: Marc Rußwurm, Marco Körner
Date: 2018-10-28 00:00:00
Tasks: Classification, Earth Observation, General Classification, Segmentation Of Remote Sensing Imagery

Clouds frequently cover the Earth's surface and pose an omnipresent challenge
to optical Earth observation methods. The vast majority of remote sensing
approaches either selectively choose single cloud-free observations or employ a
pre-classification strategy to identify and mask cloudy pixels. We follow a
different strategy and treat cloud coverage as noise that is inherent to the
observed satellite data. In prior work, we directly employed a straightforward
\emph{convolutional long short-term memory} network for vegetation
classification without explicit cloud filtering and achieved state-of-the-art
classification accuracies. In this work, we investigate this cloud-robustness
further by visualizing internal cell activations and performing an ablation
experiment on datasets of different cloud coverage. In the visualizations of
network states, we identified some cells in which modulation and input gates
closed on cloudy pixels. This indicates that the network has internalized a
cloud-filtering mechanism without being specifically trained on cloud labels.
Overall, our results question the necessity of sophisticated pre-processing
pipelines for multi-temporal deep learning approaches.
Unsupervised Post-processing of Word Vectors via Conceptor Negation
Authors: Tianlin Liu, Lyle Ungar, João Sedoc
Date: 2018-11-17 00:00:00
Tasks: Dialogue State Tracking, Negation

Word vectors are at the core of many natural language processing tasks.
Recently, there has been interest in post-processing word vectors to enrich
their semantic information. In this paper, we introduce a novel word vector
post-processing technique based on matrix conceptors (Jaeger2014), a family of
regularized identity maps. More concretely, we propose to use conceptors to
suppress those latent features of word vectors having high variances. The
proposed method is purely unsupervised: it does not rely on any corpus or
external linguistic database. We evaluate the post-processed word vectors on a
battery of intrinsic lexical evaluation tasks, showing that the proposed method
consistently outperforms existing state-of-the-art alternatives. We also show
that post-processed word vectors can be used for the downstream natural
language processing task of dialogue state tracking, yielding improved results
in different dialogue domains.
Anchor Box Optimization for Object Detection
Authors: Yuanyi Zhong, Jian-Feng Wang, Jian Peng, Lei Zhang
Date: 2018-12-02 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper, we propose a general approach to optimize anchor boxes for object detection. Nowadays, anchor boxes are widely adopted in state-of-the-art detection frameworks. However, these frameworks usually pre-define anchor box shapes in heuristic ways and fix the sizes during training. To improve the accuracy and reduce the effort of designing anchor boxes, we propose to dynamically learn the anchor shapes, which allows the anchors to automatically adapt to the data distribution and the network learning capability. The learning approach can be easily implemented with stochastic gradient descent and can be plugged into any anchor box-based detection framework. The extra training cost is almost negligible and it has no impact on the inference time or memory cost. Exhaustive experiments demonstrate that the proposed anchor optimization method consistently achieves significant improvement ($\ge 1\%$ mAP absolute gain) over the baseline methods on several benchmark datasets including Pascal VOC 07+12, MS COCO and Brainwash. Meanwhile, the robustness is also verified towards different anchor initialization methods and the number of anchor shapes, which greatly simplifies the problem of anchor box design.
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
Authors: JIyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia
Date: 2018-12-01 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection, Semi-Supervised Object Detection, Weakly Supervised Object Detection

The labeling cost of large number of bounding boxes is one of the main
challenges for training modern object detectors. To reduce the dependence on
expensive bounding box annotations, we propose a new semi-supervised object
detection formulation, in which a few seed box level annotations and a large
scale of image level annotations are used to train the detector. We adopt a
training-mining framework, which is widely used in weakly supervised object
detection tasks. However, the mining process inherently introduces various
kinds of labelling noises: false negatives, false positives and inaccurate
boundaries, which can be harmful for training the standard object detectors
(e.g. Faster RCNN). We propose a novel NOise Tolerant Ensemble RCNN (NOTE-RCNN)
object detector to handle such noisy labels. Comparing to standard Faster RCNN,
it contains three highlights: an ensemble of two classification heads and a
distillation head to avoid overfitting on noisy labels and improve the mining
precision, masking the negative sample loss in box predictor to avoid the harm
of false negative labels, and training box regression head only on seed
annotations to eliminate the harm from inaccurate boundaries of mined bounding
boxes. We evaluate the methods on ILSVRC 2013 and MSCOCO 2017 dataset; we
observe that the detection accuracy consistently improves as we iterate between
mining and training steps, and state-of-the-art performance is achieved.
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
Authors: Jian Ding, Nan Xue, Yang Long, Gui-Song Xia, Qikai Lu
Date: 2018-12-01 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection, Object Detection In Aerial Images, Oriented Object Detection, Position

Object detection in aerial images is an active yet challenging task in
computer vision because of the birdview perspective, the highly complex
backgrounds, and the variant appearances of objects. Especially when detecting
densely packed objects in aerial images, methods relying on horizontal
proposals for common object detection often introduce mismatches between the
Region of Interests (RoIs) and objects. This leads to the common misalignment
between the final object classification confidence and localization accuracy.
Although rotated anchors have been used to tackle this problem, the design of
them always multiplies the number of anchors and dramatically increases the
computational complexity. In this paper, we propose a RoI Transformer to
address these problems. More precisely, to improve the quality of region
proposals, we first designed a Rotated RoI (RRoI) learner to transform a
Horizontal Region of Interest (HRoI) into a Rotated Region of Interest (RRoI).
Based on the RRoIs, we then proposed a Rotated Position Sensitive RoI Align
(RPS-RoI-Align) module to extract rotation-invariant features from them for
boosting subsequent classification and regression. Our RoI Transformer is with
light weight and can be easily embedded into detectors for oriented object
detection. A simple implementation of the RoI Transformer has achieved
state-of-the-art performances on two common and challenging aerial datasets,
i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our
RoI Transformer exceeds the deformable Position Sensitive RoI pooling when
oriented bounding-box annotations are available. Extensive experiments have
also validated the flexibility and effectiveness of our RoI Transformer. The
results demonstrate that it can be easily integrated with other detector
architectures and significantly improve the performances.
What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection
Authors: Eduardo Ruiz, Walterio Mayol-Cuevas
Date: 2018-12-03 00:00:00
Tasks: Affordance Detection, Multiple Affordance Detection, One-Shot Learning

This paper develops and evaluates a novel method that allows for the
detection of affordances in a scalable and multiple-instance manner on visually
recovered pointclouds. Our approach has many advantages over alternative
methods, as it is based on highly parallelizable, one-shot learning that is
fast in commodity hardware. The approach is hybrid in that it uses a geometric
representation together with a state-of-the-art deep learning method capable of
identifying 3D scene saliency. The geometric component allows for a compact and
efficient representation, boosting the performance of the deep network
architecture which proved insufficient on its own. Moreover, our approach
allows not only to predict whether an input scene affords or not the
interactions, but also the pose of the objects that allow these interactions to
take place. Our predictions align well with crowd-sourced human judgment as
they are preferred with 87% probability, show high rates of improvement with
almost four times (4x) better performance over a deep learning-only baseline
and are seven times (7x) faster than previous art.
Learning 3D Human Dynamics from Video
Authors: Angjoo Kanazawa, Jason Y. Zhang, Panna Felsen, Jitendra Malik
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Dynamics, 3D Human Pose Estimation, Human Dynamics

From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model, Human Mesh and Motion Recovery (HMMR), on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video, code, and data can be found at https://akanazawa.github.io/human_dynamics/.
AutoFocus: Efficient Multi-Scale Inference
Authors: Mahyar Najibi, Bharat Singh, Larry S. Davis
Date: 2018-12-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: GPU

This paper describes AutoFocus, an efficient multi-scale inference algorithm for deep-learning based object detectors. Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions which are likely to contain small objects at finer scales. This is achieved by predicting category agnostic segmentation maps for small objects at coarser scales, called FocusPixels. FocusPixels can be predicted with high recall, and in many cases, they only cover a small fraction of the entire image. To make efficient use of FocusPixels, an algorithm is proposed which generates compact rectangular FocusChips which enclose FocusPixels. The detector is only applied inside FocusChips, which reduces computation while processing finer scales. Different types of error can arise when detections from FocusChips of multiple scales are combined, hence techniques to correct them are proposed. AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU. This is 2.5X faster than our multi-scale baseline detector and matches its mAP. The number of pixels processed in the pyramid can be reduced by 5X with a 1% drop in mAP. AutoFocus obtains more than 10% mAP gain compared to RetinaNet but runs at the same speed with the same ResNet-101 backbone.
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
Authors: Donglai Xiang, Hanbyul Joo, Yaser Sheikh
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, Hand Pose Estimation, Monocular 3D Human Pose Estimation

We present the first method to capture the 3D total motion of a target person
from a monocular view input. Given an image or a monocular video, our method
reconstructs the motion from body, face, and fingers represented by a 3D
deformable mesh model. We use an efficient representation called 3D Part
Orientation Fields (POFs), to encode the 3D orientations of all body parts in
the common 2D image space. POFs are predicted by a Fully Convolutional Network
(FCN), along with the joint confidence maps. To train our network, we collect a
new 3D human motion dataset capturing diverse total body motion of 40 subjects
in a multiview system. We leverage a 3D deformable human model to reconstruct
total body pose from the CNN outputs by exploiting the pose and shape prior in
the model. We also present a texture-based tracking method to obtain temporally
coherent motion capture output. We perform thorough quantitative evaluations
including comparison with the existing body-specific and hand-specific methods,
and performance analysis on camera viewpoint and human pose changes. Finally,
we demonstrate the results of our total body motion capture on various
challenging in-the-wild videos. Our code and newly collected human motion
dataset will be publicly shared.
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Authors: Yi Zhu, Karan Sapra, Fitsum A. Reda, Kevin J. Shih, Shawn Newsam, Andrew Tao, Bryan Catanzaro
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Segmentation, Semantic Segmentation, Video Propagation

Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks. We exploit video prediction models' ability to predict future frames in order to also predict future labels. A joint propagation strategy is also proposed to alleviate mis-alignments in synthesized samples. We demonstrate that training segmentation models on datasets augmented by the synthesized samples leads to significant improvements in accuracy. Furthermore, we introduce a novel boundary label relaxation technique that makes training robust to annotation noise and propagation artifacts along object boundaries. Our proposed methods achieve state-of-the-art mIoUs of 83.5% on Cityscapes and 82.9% on CamVid. Our single model, without model ensembles, achieves 72.8% mIoU on the KITTI semantic segmentation test set, which surpasses the winning entry of the ROB challenge 2018. Our code and videos can be found at https://nv-adlr.github.io/publication/2018-Segmentation.
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
Authors: Marvin Teichmann, Andre Araujo, Menglong Zhu, Jack Sim
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Retrieval, Retrieval

Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the index that focuses on the most relevant regions. However, due to the lack of bounding-box datasets for objects of interest among retrieval benchmarks, most recent work on regional representations has focused on either uniform or class-agnostic region selection. In this paper, we first fill the void by providing a new dataset of landmark bounding boxes, based on the Google Landmarks dataset, that includes $86k$ images with manually curated boxes from $15k$ unique landmarks. Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods. In addition, we introduce a novel regional aggregated selective match kernel (R-ASMK) to effectively combine information from detected regions into an improved holistic image representation. R-ASMK boosts image retrieval accuracy substantially with no dimensionality increase, while even outperforming systems that index image regions independently. Our complete image retrieval system improves upon the previous state-of-the-art by significant margins on the Revisited Oxford and Paris datasets. Code and data available at the project webpage: https://github.com/tensorflow/models/tree/master/research/delf.
Nested Reasoning About Autonomous Agents Using Probabilistic Programs
Authors: Iris Rubi Seaman, Jan-Willem van de Meent, David Wingate
Date: 2018-12-04 00:00:00
Tasks: Probabilistic Programming

As autonomous agents become more ubiquitous, they will eventually have to reason about the plans of other agents, which is known as theory of mind reasoning. We develop a planning-as-inference framework in which agents perform nested simulation to reason about the behavior of other agents in an online manner. As a concrete application of this framework, we use probabilistic programs to model a high-uncertainty variant of pursuit-evasion games in which an agent must make inferences about the other agents' plans to craft counter-plans. Our probabilistic programs incorporate a variety of complex primitives such as field-of-view calculations and path planners, which enable us to model quasi-realistic scenarios in a computationally tractable manner. We perform extensive experimental evaluations which establish a variety of rational behaviors and quantify how allocating computation across levels of nesting affects the variance of our estimators.