pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Graph Convolutional Networks for 3D Human Pose Regression
Authors: Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris N. Metaxas
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, regression

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
Authors: Cheoneum Park, Juae Kim, Hyeon-gu Lee, Reinald Kim Amplayo, Harksoo Kim, Jungyun Seo, Chang-Ki Lee
Date: 2019-04-06 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentence, Suggestion mining, Translation, Word Embeddings

This paper describes our system, Joint Encoders for Stable Suggestion
Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online
Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one
using multiple pre-trained word embeddings learned from log-bilinear regression
(GloVe) and translation (CoVe) models, and (b) one on top of word encodings
from a pre-trained deep bidirectional transformer (BERT). We include a domain
adversarial training module when training for out-of-domain samples. Our
experiments show that while BERT performs exceptionally well for in-domain
samples, several runs of the model show that it is unstable for out-of-domain
samples. The problem is mitigated tremendously by (1) combining BERT with a
non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our
final models obtained second place with 77.78\% F-Score on Subtask A (i.e.
in-domain) and achieved an F-Score of 79.59\% on Subtask B (i.e.
out-of-domain), even without using any additional external data.
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Authors: Nicolas Garcia Trillos, Daniel Sanz-Alonso, Ruiyi Yang
Date: 2019-04-06 00:00:00

Several data analysis techniques employ similarity relationships between data
points to uncover the intrinsic dimension and geometric structure of the
underlying data-generating mechanism. In this paper we work under the model
assumption that the data is made of random perturbations of feature vectors
lying on a low-dimensional manifold. We study two questions: how to define the
similarity relationship over noisy data points, and what is the resulting
impact of the choice of similarity in the extraction of global geometric
information from the underlying manifold. We provide concrete mathematical
evidence that using a local regularization of the noisy data to define the
similarity improves the approximation of the hidden Euclidean distance between
unperturbed points. Furthermore, graph-based objects constructed with the
locally regularized similarity function satisfy better error bounds in their
recovery of global geometric ones. Our theory is supported by numerical
experiments that demonstrate that the gain in geometric understanding
facilitated by local regularization translates into a gain in classification
accuracy in simulated and real data.
360 Panorama Synthesis from a Sparse Set of Images with Unknown Field of View
Authors: Julius Surya Sumantri, In Kyu Park
Date: 2019-04-06 00:00:00
Tasks: Navigate

360 images represent scenes captured in all possible viewing directions and enable viewers to navigate freely around the scene thereby providing an immersive experience. Conversely, conventional images represent scenes in a single viewing direction with a small or limited field of view (FOV). As a result, only certain parts of the scenes are observed, and valuable information about the surroundings is lost. In this paper, a learning-based approach that reconstructs the scene in 360 x 180 from a sparse set of conventional images (typically 4 images) is proposed. The proposed approach first estimates the FOV of input images relative to the panorama. The estimated FOV is then used as the prior for synthesizing a high-resolution 360 panoramic output. The proposed method overcomes the difficulty of learning-based approach in synthesizing high resolution images (up to 512$\times$1024). Experimental results demonstrate that the proposed method produces 360 panorama with reasonable quality. Results also show that the proposed method outperforms the alternative method and can be generalized for non-panoramic scenes and images captured by a smartphone camera.
Publicly Available Clinical BERT Embeddings
Authors: Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, Matthew B. A. McDermott
Date: 2019-04-06 00:00:00
Proceeding: WS 2019 6
Tasks: De-identification

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on three common clinical NLP tasks as compared to nonspecific embeddings. These domain-specific models are not as performant on two clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.
Gender Bias in Contextualized Word Embeddings
Authors: Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, Kai-Wei Chang
Date: 2019-04-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Word Embeddings

In this paper, we quantify, analyze and mitigate gender bias exhibited in
ELMo's contextualized word vectors. First, we conduct several intrinsic
analyses and find that (1) training data for ELMo contains significantly more
male than female entities, (2) the trained ELMo embeddings systematically
encode gender information and (3) ELMo unequally encodes gender information
about male and female entities. Then, we show that a state-of-the-art
coreference system that depends on ELMo inherits its bias and demonstrates
significant bias on the WinoBias probing corpus. Finally, we explore two
methods to mitigate such gender bias and show that the bias demonstrated on
WinoBias can be eliminated.
Convolutional Relational Machine for Group Activity Recognition
Authors: Sina Mokhtarzadeh Azar, Mina Ghadimi Atigh, Ahmad Nickabadi, Alexandre Alahi
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Activity Recognition, Group Activity Recognition

We present an end-to-end deep Convolutional Neural Network called
Convolutional Relational Machine (CRM) for recognizing group activities that
utilizes the information in spatial relations between individual persons in
image or video. It learns to produce an intermediate spatial representation
(activity map) based on individual and group activities. A multi-stage
refinement component is responsible for decreasing the incorrect predictions in
the activity map. Finally, an aggregation component uses the refined
information to recognize group activities. Experimental results demonstrate the
constructive contribution of the information extracted and represented in the
form of the activity map. CRM shows advantages over state-of-the-art models on
Volleyball and Collective Activity datasets.
Effective Context and Fragment Feature Usage for Named Entity Recognition
Authors: Nargiza Nosirova, MingBin Xu, Hui Jiang
Date: 2019-04-05 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, Word Embeddings

In this paper, we explore a new approach to named entity recognition (NER)
with the goal of learning from context and fragment features more effectively,
contributing to the improvement of overall recognition performance. We use the
recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode
each sentence fragment and its left-right contexts into a fixed-size
representation. Next, we organize the context and fragment features into
groups, and feed each feature group to dedicated fully-connected layers.
Finally, we merge each group's final dedicated layers and add a shared layer
leading to a single output. The outcome of our experiments show that, given
only tokenized text and trained word embeddings, our system outperforms our
baseline models, and is competitive to the state-of-the-arts of various
well-known NER tasks.
Revealing Scenes by Inverting Structure from Motion Reconstructions
Authors: Francesco Pittaluga, Sanjeev J. Koppal, Sing Bing Kang, Sudipta N. Sinha
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6

Many 3D vision systems localize cameras within a scene using 3D point clouds.
Such point clouds are often obtained using structure from motion (SfM), after
which the images are discarded to preserve privacy. In this paper, we show, for
the first time, that such point clouds retain enough information to reveal
scene appearance and compromise privacy. We present a privacy attack that
reconstructs color images of the scene from the point cloud. Our method is
based on a cascaded U-Net that takes as input, a 2D multichannel image of the
points rendered from a specific viewpoint containing point depth and optionally
color and SIFT descriptors and outputs a color image of the scene from that
viewpoint. Unlike previous feature inversion methods, we deal with highly
sparse and irregular 2D point distributions and inputs where many point
attributes are missing, namely keypoint orientation and scale, the descriptor
image source and the 3D point visibility. We evaluate our attack algorithm on
public datasets and analyze the significance of the point cloud attributes.
Finally, we show that novel views can also be generated thereby enabling
compelling virtual tours of the underlying scene.
A Multi-task Learning Approach for Named Entity Recognition using Local Detection
Authors: Nargiza Nosirova, MingBin Xu, Hui Jiang
Date: 2019-04-05 00:00:00
Tasks: Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER) systems that perform well require task-related
and manually annotated datasets. However, they are expensive to develop, and
are thus limited in size. As there already exists a large number of NER
datasets that share a certain degree of relationship but differ in content, it
is important to explore the question of whether such datasets can be combined
as a simple method for improving NER performance. To investigate this, we
developed a novel locally detecting multitask model using FFNNs. The model
relies on encoding variable-length sequences of words into theoretically
lossless and unique fixed-size representations. We applied this method to
several well-known NER tasks and compared the results of our model to baseline
models as well as other published results. As a result, we observed competitive
performance in nearly all of the tasks.
A General Framework for Information Extraction using Dynamic Span Graphs
Authors: Yi Luan, Dave Wadden, Luheng He, Amy Shah, Mari Ostendorf, Hannaneh Hajishirzi
Date: 2019-04-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Joint Entity and Relation Extraction, Named Entity Recognition (NER), Relation, Relation Extraction

We introduce a general framework for several information extraction tasks
that share span representations using dynamically constructed span graphs. The
graphs are constructed by selecting the most confident entity spans and linking
these nodes with confidence-weighted relation types and coreferences. The
dynamic span graph allows coreference and relation type confidences to
propagate through the graph to iteratively refine the span representations.
This is unlike previous multi-task frameworks for information extraction in
which the only interaction between tasks is in the shared first-layer LSTM. Our
framework significantly outperforms the state-of-the-art on multiple
information extraction tasks across multiple datasets reflecting different
domains. We further observe that the span enumeration approach is good at
detecting nested span entities, with significant F1 score improvement on the
ACE dataset.
Multi-Preference Actor Critic
Authors: Ishan Durugkar, Matthew Hausknecht, Adith Swaminathan, Patrick MacAlpine
Date: 2019-04-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient algorithms typically combine discounted future rewards with
an estimated value function, to compute the direction and magnitude of
parameter updates. However, for most Reinforcement Learning tasks, humans can
provide additional insight to constrain the policy learning. We introduce a
general method to incorporate multiple different feedback channels into a
single policy gradient loss. In our formulation, the Multi-Preference Actor
Critic (M-PAC), these different types of feedback are implemented as
constraints on the policy. We use a Lagrangian relaxation to satisfy these
constraints using gradient descent while learning a policy that maximizes
rewards. Experiments in Atari and Pendulum verify that constraints are being
respected and can accelerate the learning process.
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits
Authors: Chao Tao, Qin Zhang, Yuan Zhou
Date: 2019-04-05 00:00:00
Tasks: Multi-Armed Bandits

Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning. In this paper we study the distributed version of this problem where we have multiple agents, and they want to learn the best arm collaboratively. We want to quantify the power of collaboration under limited interaction (or, communication steps), as interaction is expensive in many settings. We measure the running time of a distributed algorithm as the speedup over the best centralized algorithm where there is only one agent. We give almost tight round-speedup tradeoffs for this problem, along which we develop several new techniques for proving lower bounds on the number of communication steps under time or confidence constraints.
The Information Complexity of Learning Tasks, their Structure and their Distance
Authors: Alessandro Achille, Giovanni Paolini, Glen Mbeng, Stefano Soatto
Date: 2019-04-05 00:00:00
Tasks: Memorization, Transfer Learning

We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine-tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training dataset, and allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity, Shannon, and Fisher Information. However, unlike some of those frameworks, it can be applied to large-scale models and real-world datasets. Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in Deep Learning.
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
Authors: Ikhsanul Habibie, Weipeng Xu, Dushyant Mehta, Gerard Pons-Moll, Christian Theobalt
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Diversity, Monocular 3D Human Pose Estimation, Pose Estimation

Convolutional Neural Network based approaches for monocular 3D human pose
estimation usually require a large amount of training images with 3D pose
annotations. While it is feasible to provide 2D joint annotations for large
corpora of in-the-wild images with humans, providing accurate 3D annotations to
such in-the-wild corpora is hardly feasible in practice. Most existing 3D
labelled data sets are either synthetically created or feature in-studio
images. 3D pose estimation algorithms trained on such data often have limited
ability to generalize to real world scene diversity. We therefore propose a new
deep learning based method for monocular 3D human pose estimation that shows
high accuracy and generalizes better to in-the-wild scenes. It has a network
architecture that comprises a new disentangled hidden space encoding of
explicit 2D and 3D features, and uses supervision by a new learned projection
model from predicted 3D pose. Our algorithm can be jointly trained on image
data with 3D labels and image data with only 2D labels. It achieves
state-of-the-art accuracy on challenging in-the-wild data.
Jasper: An End-to-End Convolutional Neural Acoustic Model
Authors: Jason Li, Vitaly Lavrukhin, Boris Ginsburg, Ryan Leary, Oleksii Kuchaiev, Jonathan M. Cohen, Huyen Nguyen, Ravi Teja Gadde
Date: 2019-04-05 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Speech Recognition

In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data. Our model, Jasper, uses only 1D convolutions, batch normalization, ReLU, dropout, and residual connections. To improve training, we further introduce a new layer-wise optimizer called NovoGrad. Through experiments, we demonstrate that the proposed deep architecture performs as well or better than more complex choices. Our deepest Jasper variant uses 54 convolutional layers. With this architecture, we achieve 2.95% WER using a beam-search decoder with an external neural language model and 3.86% WER with a greedy decoder on LibriSpeech test-clean. We also report competitive results on the Wall Street Journal and the Hub5'00 conversational evaluation datasets.
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
Authors: Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran, Giedrius Burachas
Date: 2019-04-05 00:00:00
Tasks: Image Retrieval, Question Answering, Retrieval, Visual Question Answering, Visual Question Answering (VQA)

While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA). In our proposed ExAG, a human user needs to guess a secret image picked by the VQA agent by asking natural language questions to it. We show that overall, when AI explains its answers, users succeed more often in guessing the secret image correctly. Notably, a few correct explanations can readily improve human performance when VQA answers are mostly incorrect as compared to no-explanation games. Furthermore, we also show that while explanations rated as "helpful" significantly improve human performance, "incorrect" and "unhelpful" explanations can degrade performance as compared to no-explanation games. Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.
Weakly Supervised Video Moment Retrieval From Text Queries
Authors: Niluthpol Chowdhury Mithun, Sujoy Paul, Amit K. Roy-Chowdhury
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Moment Retrieval, Natural Language Queries, Retrieval, Sentence

There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scalable. In order to cope with this issue, in this work, we introduce the problem of learning from weak labels for the task of text to video moment retrieval. The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate. We propose a joint visual-semantic embedding based framework that learns the notion of relevant segments from video using only video-level sentence descriptions. Specifically, our main idea is to utilize latent alignment between video frames and sentence descriptions using Text-Guided Attention (TGA). TGA is then used during the test phase to retrieve relevant moments. Experiments on two benchmark datasets demonstrate that our method achieves comparable performance to state-of-the-art fully supervised approaches.
Prediction-Tracking-Segmentation
Authors: Jianren Wang, Yihui He, Xiaobo Wang, Xinjia Yu, Xia Chen
Date: 2019-04-05 00:00:00
Tasks: Prediction, Segmentation, Video Segmentation, Video Semantic Segmentation, Visual Tracking

We introduce a prediction driven method for visual tracking and segmentation
in videos. Instead of solely relying on matching with appearance cues for
tracking, we build a predictive model which guides finding more accurate
tracking regions efficiently. With the proposed prediction mechanism, we
improve the model robustness against distractions and occlusions during
tracking. We demonstrate significant improvements over state-of-the-art methods
not only on visual tracking tasks (VOT 2016 and VOT 2018) but also on video
segmentation datasets (DAVIS 2016 and DAVIS 2017).