pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Authors: Jingqing Zhang, Piyawat Lertvittayakumjorn, Yike Guo
Date: 2019-03-29 00:00:00
Proceeding: NAACL 2019 6
Tasks: Classification, Data Augmentation, General Classification, General Knowledge, text-classification, Text Classification, Word Embeddings, Zero-Shot Text Classification

Insufficient or even unavailable training data of emerging classes is a big
challenge of many classification tasks, including text classification.
Recognising text documents of classes that have never been seen in the learning
stage, so-called zero-shot text classification, is therefore difficult and only
limited previous works tackled this problem. In this paper, we propose a
two-phase framework together with data augmentation and feature augmentation to
solve this problem. Four kinds of semantic knowledge (word embeddings, class
descriptions, class hierarchy, and a general knowledge graph) are incorporated
into the proposed framework to deal with instances of unseen classes
effectively. Experimental results show that each and the combination of the two
phases achieve the best overall accuracy compared with baselines and recent
approaches in classifying real-world texts under the zero-shot scenario.
Resample-smoothing of Voronoi intensity estimators
Authors: M. Mehdi Moradi, Ottmar Cronie, Ege Rubak, Raphael Lachieze-Rey, Jorge Mateu, Adrian Baddeley.
Date: 2019-01-19 00:00:00
Proceeding: Journal of Statistics and Computing 2019 1

Voronoi estimators are non-parametric and adaptive estimators of the intensity of a point process. The intensity estimate at a given location is equal to the reciprocal of the size of the Voronoi/Dirichlet cell containing that location. Their major drawback is that they tend to paradoxically under-smooth the data in regions where the point density of the observed point pattern is high, and over-smooth where the point density is low. To remedy this behaviour, we propose to apply an additional smoothing operation to the Voronoi estimator, based on resampling the point pattern by independent random thinning. Through a simulation study we show that our resample-smoothing technique improves the estimation substantially. In addition, we study statistical properties such as unbiasedness and variance, and propose a rule-of-thumb and a data-driven cross-validation approach to choose the amount of smoothing to apply. Finally we apply our proposed intensity estimation scheme to two datasets: locations of pine saplings (planar point pattern) and motor vehicle traffic accidents (linear network point pattern). 

The code implementation can be found in the R package spatstat using the function "adaptive.density".
Deep Attention-guided Hashing
Authors: Zhan Yang, Osolo Ian Raymond, Wuqing Sun, Jun Long
Date: 2018-12-04 00:00:00
Tasks: Deep Attention

With the rapid growth of multimedia data (e.g., image, audio and video etc.)
on the web, learning-based hashing techniques such as Deep Supervised Hashing
(DSH) have proven to be very efficient for large-scale multimedia search. The
recent successes seen in Learning-based hashing methods are largely due to the
success of deep learning-based hashing methods. However, there are some
limitations to previous learning-based hashing methods (e.g., the learned hash
codes containing repetitive and highly correlated information). In this paper,
we propose a novel learning-based hashing method, named Deep Attention-guided
Hashing (DAgH). DAgH is implemented using two stream frameworks. The core idea
is to use guided hash codes which are generated by the hashing network of the
first stream framework (called first hashing network) to guide the training of
the hashing network of the second stream framework (called second hashing
network). Specifically, in the first network, it leverages an attention network
and hashing network to generate the attention-guided hash codes from the
original images. The loss function we propose contains two components: the
semantic loss and the attention loss. The attention loss is used to punish the
attention network to obtain the salient region from pairs of images; in the
second network, these attention-guided hash codes are used to guide the
training of the second hashing network (i.e., these codes are treated as
supervised labels to train the second network). By doing this, DAgH can make
full use of the most critical information contained in images to guide the
second hashing network in order to learn efficient hash codes in a true
end-to-end fashion. Results from our experiments demonstrate that DAgH can
generate high quality hash codes and it outperforms current state-of-the-art
methods on three benchmark datasets, CIFAR-10, NUS-WIDE, and ImageNet.
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes
Authors: Nicholas Heller, Niranjan Sathianathen, Arveen Kalapara, Edward Walczak, Keenan Moore, Heather Kaluzniak, Joel Rosenberg, Paul Blake, Zachary Rengel, Makinna Oestreich, Joshua Dean, Michael Tradewell, Aneri Shah, Resha Tejpaul, Zachary Edgerton, Matthew Peterson, Shaneabbas Raza, Subodh Regmi, Nikolaos Papanikolopoulos, Christopher Weight
Date: 2019-03-31 00:00:00
Tasks: Benchmarking, Computed Tomography (CT), Decision Making, Segmentation, Semantic Segmentation, Tumor Segmentation

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Automatic semantic segmentation of kidneys and kidney tumors is a promising tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task. We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge and have been released publicly. With the presence of clinical context and surgical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging and semantic segmentation masks.
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
Authors: Yingwei Li, Song Bai, Cihang Xie, Zhenyu Liao, Xiaohui Shen, Alan L. Yuille
Date: 2019-04-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: object-detection, Object Detection, Semantic Segmentation

This paper focuses on learning transferable adversarial examples specifically against defense models (models to defense adversarial attacks). In particular, we show that a simple universal perturbation can fool a series of state-of-the-art defenses. Adversarial examples generated by existing attacks are generally hard to transfer to defense models. We observe the property of regional homogeneity in adversarial perturbations and suggest that the defenses are less robust to regionally homogeneous perturbations. Therefore, we propose an effective transforming paradigm and a customized gradient transformer module to transform existing perturbations into regionally homogeneous ones. Without explicitly forcing the perturbations to be universal, we observe that a well-trained gradient transformer module tends to output input-independent gradients (hence universal) benefiting from the under-fitting phenomenon. Thorough experiments demonstrate that our work significantly outperforms the prior art attacking algorithms (either image-dependent or universal ones) by an average improvement of 14.0% when attacking 9 defenses in the transfer-based attack setting. In addition to the cross-model transferability, we also verify that regionally homogeneous perturbations can well transfer across different vision tasks (attacking with the semantic segmentation task and testing on the object detection task). The code is available here: https://github.com/LiYingwei/Regional-Homogeneity.
A Practical Algorithm for Topic Modeling with Provable Guarantees
Authors: Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu
Date: 2012-12-19 00:00:00
Tasks: Dimensionality Reduction, Topic Models

Topic models provide a useful method for dimensionality reduction and
exploratory data analysis in large text corpora. Most approaches to topic model
inference have been based on a maximum likelihood objective. Efficient
algorithms exist that approximate this objective, but they have no provable
guarantees. Recently, algorithms have been introduced that provide provable
bounds, but these algorithms are not practical because they are inefficient and
not robust to violations of model assumptions. In this paper we present an
algorithm for topic model inference that is both provable and practical. The
algorithm produces results comparable to the best MCMC implementations while
running orders of magnitude faster.
Learning Topic Models - Going beyond SVD
Authors: Sanjeev Arora, Rong Ge, Ankur Moitra
Date: 2012-04-09 00:00:00
Tasks: Topic Models

Topic Modeling is an approach used for automatic comprehension and
classification of data in a variety of settings, and perhaps the canonical
application is in uncovering thematic structure in a corpus of documents. A
number of foundational works both in machine learning and in theory have
suggested a probabilistic model for documents, whereby documents arise as a
convex combination of (i.e. distribution on) a small number of topic vectors,
each topic vector being a distribution on words (i.e. a vector of
word-frequencies). Similar models have since been used in a variety of
application areas; the Latent Dirichlet Allocation or LDA model of Blei et al.
is especially popular.
  Theoretical studies of topic modeling focus on learning the model's
parameters assuming the data is actually generated from it. Existing approaches
for the most part rely on Singular Value Decomposition(SVD), and consequently
have one of two limitations: these works need to either assume that each
document contains only one topic, or else can only recover the span of the
topic vectors instead of the topic vectors themselves.
  This paper formally justifies Nonnegative Matrix Factorization(NMF) as a main
tool in this context, which is an analog of SVD where all vectors are
nonnegative. Using this tool we give the first polynomial-time algorithm for
learning topic models without the above two limitations. The algorithm uses a
fairly mild assumption about the underlying topic matrix called separability,
which is usually found to hold in real-life data. A compelling feature of our
algorithm is that it generalizes to models that incorporate topic-topic
correlations, such as the Correlated Topic Model and the Pachinko Allocation
Model.
  We hope that this paper will motivate further theoretical results that use
NMF as a replacement for SVD - just as NMF has come to replace SVD in many
applications.
SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
Authors: Diogo Pernes, Jaime S. Cardoso
Date: 2019-03-31 00:00:00

We propose a framework to model the distribution of sequential data coming
from a set of entities connected in a graph with a known topology. The method
is based on a mixture of shared hidden Markov models (HMMs), which are jointly
trained in order to exploit the knowledge of the graph structure and in such a
way that the obtained mixtures tend to be sparse. Experiments in different
application domains demonstrate the effectiveness and versatility of the
method.
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements
Authors: Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, Hua Huang
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Reflection Removal

Removing undesirable reflections from a single image captured through a glass
window is of practical importance to visual computing systems. Although
state-of-the-art methods can obtain decent results in certain situations,
performance declines significantly when tackling more general real-world cases.
These failures stem from the intrinsic difficulty of single image reflection
removal -- the fundamental ill-posedness of the problem, and the insufficiency
of densely-labeled training data needed for resolving this ambiguity within
learning-based neural network pipelines. In this paper, we address these issues
by exploiting targeted network enhancements and the novel use of misaligned
data. For the former, we augment a baseline network architecture by embedding
context encoding modules that are capable of leveraging high-level contextual
clues to reduce indeterminacy within areas containing strong reflections. For
the latter, we introduce an alignment-invariant loss function that facilitates
exploiting misaligned real-world training data that is much easier to collect.
Experimental results collectively show that our method outperforms the
state-of-the-art with aligned data, and that significant improvements are
possible when using additional misaligned data.
NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences
Authors: Chen Zhao, Zhiguo Cao, Chi Li, Xin Li, Jiaqi Yang
Date: 2019-03-31 00:00:00
Proceeding: CVPR 2019 6

Feature correspondence selection is pivotal to many feature-matching based
tasks in computer vision. Searching for spatially k-nearest neighbors is a
common strategy for extracting local information in many previous works.
However, there is no guarantee that the spatially k-nearest neighbors of
correspondences are consistent because the spatial distribution of false
correspondences is often irregular. To address this issue, we present a
compatibility-specific mining method to search for consistent neighbors.
Moreover, in order to extract and aggregate more reliable features from
neighbors, we propose a hierarchical network named NM-Net with a series of
convolution layers taking the generated graph as input, which is insensitive to
the order of correspondences. Our experimental results have shown the proposed
method achieves the state-of-the-art performance on four datasets with various
inlier ratios and varying numbers of feature consistencies.
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks
Authors: Xijun Wang, Meina Kan, Shiguang Shan, Xilin Chen
Date: 2019-03-31 00:00:00
Proceeding: CVPR 2019 6
Tasks: CPU

Benefitted from its great success on many tasks, deep learning is
increasingly used on low-computational-cost devices, e.g. smartphone, embedded
devices, etc. To reduce the high computational and memory cost, in this work,
we propose a fully learnable group convolution module (FLGC for short) which is
quite efficient and can be embedded into any deep neural networks for
acceleration. Specifically, our proposed method automatically learns the group
structure in the training stage in a fully end-to-end manner, leading to a
better structure than the existing pre-defined, two-steps, or iterative
strategies. Moreover, our method can be further combined with depthwise
separable convolution, resulting in 5 times acceleration than the vanilla
Resnet50 on single CPU. An additional advantage is that in our FLGC the number
of groups can be set as any value, but not necessarily 2^k as in most existing
methods, meaning better tradeoff between accuracy and speed. As evaluated in
our experiments, our method achieves better performance than existing learnable
group convolution and standard group convolution when using the same number of
groups.
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification
Authors: Yifan Sun, Qin Xu, Ya-Li Li, Chi Zhang, Yikang Li, Shengjin Wang, Jian Sun
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Person Re-Identification

This paper considers a realistic problem in person re-identification (re-ID)
task, i.e., partial re-ID. Under partial re-ID scenario, the images may contain
a partial observation of a pedestrian. If we directly compare a partial
pedestrian image with a holistic one, the extreme spatial misalignment
significantly compromises the discriminative ability of the learned
representation. We propose a Visibility-aware Part Model (VPM), which learns to
perceive the visibility of regions through self-supervision. The visibility
awareness allows VPM to extract region-level features and compare two images
with focus on their shared regions (which are visible on both images). VPM
gains two-fold benefit toward higher accuracy for partial re-ID. On the one
hand, compared with learning a global feature, VPM learns region-level features
and benefits from fine-grained information. On the other hand, with visibility
awareness, VPM is capable to estimate the shared regions between two images and
thus suppresses the spatial misalignment. Experimental results confirm that our
method significantly improves the learned representation and the achieved
accuracy is on par with the state of the art.
Scene Graph Generation with External Knowledge and Image Reconstruction
Authors: Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, Mingyang Ling
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Generation, Image Reconstruction, Object, object-detection, Object Detection, Relationship Detection, Scene Graph Generation, Visual Relationship Detection

Scene graph generation has received growing attention with the advancements
in image understanding tasks such as object detection, attributes and
relationship prediction,~\etc. However, existing datasets are biased in terms
of object and relationship labels, or often come with noisy and missing
annotations, which makes the development of a reliable scene graph prediction
model very challenging. In this paper, we propose a novel scene graph
generation algorithm with external knowledge and image reconstruction loss to
overcome these dataset issues. In particular, we extract commonsense knowledge
from the external knowledge base to refine object and phrase features for
improving generalizability in scene graph generation. To address the bias of
noisy object annotations, we introduce an auxiliary image reconstruction path
to regularize the scene graph generation network. Extensive experiments show
that our framework can generate better scene graphs, achieving the
state-of-the-art performance on two benchmark datasets: Visual Relationship
Detection and Visual Genome datasets.
Multi-source weak supervision for saliency detection
Authors: Yu Zeng, Yunzhi Zhuge, Huchuan Lu, Lihe Zhang, Mingyang Qian, Yizhou Yu
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Caption Generation, Saliency Detection, Saliency Prediction

The high cost of pixel-level annotations makes it appealing to train saliency
detection models with weak supervision. However, a single weak supervision
source usually does not contain enough information to train a well-performing
model. To this end, we propose a unified framework to train saliency detection
models with diverse weak supervision sources. In this paper, we use category
labels, captions, and unlabelled data for training, yet other supervision
sources can also be plugged into this flexible framework. We design a
classification network (CNet) and a caption generation network (PNet), which
learn to predict object categories and generate captions, respectively,
meanwhile highlight the most important regions for corresponding tasks. An
attention transfer loss is designed to transmit supervision signal between
networks, such that the network designed to be trained with one supervision
source can benefit from another. An attention coherence loss is defined on
unlabelled data to encourage the networks to detect generally salient regions
instead of task-specific regions. We use CNet and PNet to generate pixel-level
pseudo labels to train a saliency prediction network (SNet). During the testing
phases, we only need SNet to predict saliency maps. Experiments demonstrate the
performance of our method compares favourably against unsupervised and weakly
supervised methods and even some supervised methods.
End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image
Authors: Seonghyeon Nam, Chongyang Ma, Menglei Chai, William Brendel, Ning Xu, Seon Joo Kim
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Generative Adversarial Network

Time-lapse videos usually contain visually appealing content but are often
difficult and costly to create. In this paper, we present an end-to-end
solution to synthesize a time-lapse video from a single outdoor image using
deep neural networks. Our key idea is to train a conditional generative
adversarial network based on existing datasets of time-lapse videos and image
sequences. We propose a multi-frame joint conditional generation framework to
effectively learn the correlation between the illumination change of an outdoor
scene and the time of the day. We further present a multi-domain training
scheme for robust training of our generative models from two datasets with
different distributions and missing timestamp labels. Compared to alternative
time-lapse video synthesis algorithms, our method uses the timestamp as the
control variable and does not require a reference video to guide the synthesis
of the final output. We conduct ablation studies to validate our algorithm and
compare with state-of-the-art techniques both qualitatively and quantitatively.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Authors: Quang-Hieu Pham, Duc Thanh Nguyen, Binh-Son Hua, Gemma Roig, Sai-Kit Yeung
Date: 2019-04-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Instance Segmentation, 3D Semantic Instance Segmentation, 3D Semantic Segmentation, Scene Understanding, Segmentation

Deep learning techniques have become the to-go models for most vision-related
tasks on 2D images. However, their power has not been fully realised on several
tasks in 3D space, e.g., 3D scene understanding. In this work, we jointly
address the problems of semantic and instance segmentation of 3D point clouds.
Specifically, we develop a multi-task pointwise network that simultaneously
performs two tasks: predicting the semantic classes of 3D points and embedding
the points into high-dimensional vectors so that points of the same object
instance are represented by similar embeddings. We then propose a multi-value
conditional random field model to incorporate the semantic and instance labels
and formulate the problem of semantic and instance segmentation as jointly
optimising labels in the field model. The proposed method is thoroughly
evaluated and compared with existing methods on different indoor scene datasets
including S3DIS and SceneNN. Experimental results showed the robustness of the
proposed joint semantic-instance segmentation scheme over its single
components. Our method also achieved state-of-the-art performance on semantic
segmentation.