pwc-archive/papers-with-abstracts huggingface dataset:
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication
Authors: Jianyu Su, Stephen Adams, Peter A. Beling
Date: 2020-04-01 00:00:00
Tasks: counterfactual, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a fully cooperative multi-agent system where agents cooperate to maximize a system's utility in a partial-observable environment. We propose that multi-agent systems must have the ability to (1) communicate and understand the inter-plays between agents and (2) correctly distribute rewards based on an individual agent's contribution. In contrast, most work in this setting considers only one of the above abilities. In this study, we develop an architecture that allows for communication among agents and tailors the system's reward for each individual agent. Our architecture represents agent communication through graph convolution and applies an existing credit assignment structure, counterfactual multi-agent policy gradient (COMA), to assist agents to learn communication by back-propagation. The flexibility of the graph structure enables our method to be applicable to a variety of multi-agent systems, e.g. dynamic systems that consist of varying numbers of agents and static systems with a fixed number of agents. We evaluate our method on a range of tasks, demonstrating the advantage of marrying communication with credit assignment. In the experiments, our proposed method yields better performance than the state-of-art methods, including COMA. Moreover, we show that the communication strategies offers us insights and interpretability of the system's cooperative policies.
Deep transformation models: Tackling complex regression problems with neural network based transformation models
Authors: Beate Sick, Torsten Hothorn, Oliver Dürr
Date: 2020-04-01 00:00:00
Tasks: Deep Learning, regression

We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks, it is predominantly used to just predict a single number. This ignores the non-deterministic character of most tasks. Especially if crucial decisions are based on the predictions, like in medical applications, it is essential to quantify the prediction uncertainty. The presented deep learning transformation model estimates the whole conditional probability distribution, which is the most thorough way to capture uncertainty about the outcome. We combine ideas from a statistical transformation model (most likely transformation) with recent transformation models from deep learning (normalizing flows) to predict complex outcome distributions. The core of the method is a parameterized transformation function which can be trained with the usual maximum likelihood framework using gradient descent. The method can be combined with existing deep learning architectures. For small machine learning benchmark datasets, we report state of the art performance for most dataset and partly even outperform it. Our method works for complex input data, which we demonstrate by employing a CNN architecture on image data.
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
Authors: Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Human Pose Estimation, 3D Human Reconstruction, 3D Human Shape Estimation, 3D Object Reconstruction From A Single Image, 3D Shape Reconstruction

Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images. We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images.
Spatio-temporal Tubelet Feature Aggregation and Object Linking in Videos
Authors: Daniel Cores, Víctor M. Brea, Manuel Mucientes
Date: 2020-04-01 00:00:00
Tasks: Object, object-detection, Object Detection, Object Localization, Small Object Detection

This paper addresses the problem of how to exploit spatio-temporal information available in videos to improve the object detection precision. We propose a two stage object detector called FANet based on short-term spatio-temporal feature aggregation to give a first detection set, and long-term object linking to refine these detections. Firstly, we generate a set of short tubelet proposals containing the object in $N$ consecutive frames. Then, we aggregate RoI pooled deep features through the tubelet using a temporal pooling operator that summarizes the information with a fixed size output independent of the number of input frames. On top of that, we define a double head implementation that we feed with spatio-temporal aggregated information for spatio-temporal object classification, and with spatial information extracted from the current frame for object localization and spatial classification. Furthermore, we also specialize each head branch architecture to better perform in each task taking into account the input data. Finally, a long-term linking method builds long tubes using the previously calculated short tubelets to overcome detection errors. We have evaluated our model in the widely used ImageNet VID dataset achieving a 80.9% mAP, which is the new state-of-the-art result for single models. Also, in the challenging small object detection dataset USC-GRAD-STDdb, our proposal outperforms the single frame baseline by 5.4% mAP.
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
Authors: Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Data Augmentation, Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

Data augmentation is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for high-level vision tasks (e.g., classification) and few are studied for low-level vision tasks (e.g., image restoration). In this paper, we provide a comprehensive analysis of the existing augmentation methods applied to the super-resolution task. We find that the methods discarding or manipulating the pixels or features too much hamper the image restoration, where the spatial relationship is very important. Based on our analyses, we propose CutBlur that cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of CutBlur is to enable a model to learn not only "how" but also "where" to super-resolve an image. By doing so, the model can understand "how much", instead of blindly learning to apply super-resolution to every given pixel. Our method consistently and significantly improves the performance across various scenarios, especially when the model size is big and the data is collected under real-world environments. We also show that our method improves other low-level vision tasks, such as denoising and compression artifact removal.
Learning to Cluster Faces via Confidence and Connectivity Estimation
Authors: Lei Yang, Dapeng Chen, Xiaohang Zhan, Rui Zhao, Chen Change Loy, Dahua Lin
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Clustering, Connectivity Estimation, Face Clustering, Retrieval

Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide range of applications including face annotation and retrieval. Recent works show that supervised clustering can result in noticeable performance gain. However, they usually involve heuristic steps and require numerous overlapped subgraphs, severely restricting their accuracy and efficiency. In this paper, we propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs. Instead, we transform the clustering problem into two sub-problems. Specifically, two graph convolutional networks, named GCN-V and GCN-E, are designed to estimate the confidence of vertices and the connectivity of edges, respectively. With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters. Experiments on two large-scale benchmarks show that our method significantly improves clustering accuracy and thus performance of the recognition models trained on top, yet it is an order of magnitude more efficient than existing supervised methods.
Semantic Drift Compensation for Class-Incremental Learning
Authors: Lu Yu, Bartłomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, Joost Van de Weijer
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: class-incremental learning, Class Incremental Learning, General Classification, Incremental Learning

Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results.
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
Authors: Lucas Baier, Marcel Hofmann, Niklas Kühl, Marisa Mohr, Gerhard Satzger
Date: 2020-04-01 00:00:00
Tasks: BIG-bench Machine Learning, regression

Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time, a phenomenon called concept drift. If not handled correctly, a concept drift can lead to significant mispredictions. We explore a novel approach for concept drift handling, which depicts a strategy to switch between the application of simple and complex machine learning models for regression tasks. We assume that the approach plays out the individual strengths of each model, switching to the simpler model if a drift occurs and switching back to the complex model for typical situations. We instantiate the approach on a real-world data set of taxi demand in New York City, which is prone to multiple drifts, e.g. the weather phenomena of blizzards, resulting in a sudden decrease of taxi demand. We are able to show that our suggested approach outperforms all regarded baselines significantly.
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art
Authors: Mohammad Braei, Sebastian Wagner
Date: 2020-04-01 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Time Series, Time Series Analysis, Time Series Anomaly Detection

Anomaly detection for time-series data has been an important research field for a long time. Seminal work on anomaly detection methods has been focussing on statistical approaches. In recent years an increasing number of machine learning algorithms have been developed to detect anomalies on time-series. Subsequently, researchers tried to improve these techniques using (deep) neural networks. In the light of the increasing number of anomaly detection methods, the body of research lacks a broad comparative evaluation of statistical, machine learning and deep learning methods. This paper studies 20 univariate anomaly detection methods from the all three categories. The evaluation is conducted on publicly available datasets, which serve as benchmarks for time-series anomaly detection. By analyzing the accuracy of each method as well as the computation time of the algorithms, we provide a thorough insight about the performance of these anomaly detection approaches, alongside some general notion of which method is suited for a certain type of data.
M2m: Imbalanced Classification via Major-to-minor Translation
Authors: Jaehyung Kim, Jongheon Jeong, Jinwoo Shin
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Classification, Diversity, General Classification, imbalanced classification, Long-tail Learning, Translation

In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples (e.g., images) from more-frequent classes. This simple approach enables a classifier to learn more generalizable features of minority classes, by transferring and leveraging the diversity of the majority information. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods. The performance of our method even surpasses those of previous state-of-the-art methods for the imbalanced classification.
Total Variation Regularization for Compartmental Epidemic Models with Time-Varying Dynamics
Authors: Wenjie Zheng
Date: 2020-04-01 00:00:00
Tasks: Epidemiology

Compartmental epidemic models are among the most popular ones in epidemiology. For the parameters (e.g., the transmission rate) characterizing these models, the majority of researchers simplify them as constants, while some others manage to detect their continuous variations. In this paper, we aim at capturing, on the other hand, discontinuous variations, which better describe the impact of many noteworthy events, such as city lockdowns, the opening of field hospitals, and the mutation of the virus, whose effect should be instant. To achieve this, we balance the model's likelihood by total variation, which regulates the temporal variations of the model parameters. To infer these parameters, instead of using Monte Carlo methods, we design a novel yet straightforward optimization algorithm, dubbed Iterated Nelder--Mead, which repeatedly applies the Nelder--Mead algorithm. Experiments conducted on the simulated data demonstrate that our approach can reproduce these discontinuities and precisely depict the epidemics.
Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data
Authors: Heeyoung Kwak, Minwoo Lee, Seunghyun Yoon, Jooyoung Chang, Sangmin Park, Kyomin Jung
Date: 2020-04-01 00:00:00
Tasks: Graph Neural Network, Marketing

Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than pre-marketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develop a novel graph-based framework for ADR signal detection using healthcare claims data. We construct a Drug-disease graph with nodes representing the medical codes. The edges are given as the relationships between two codes, computed using the data. We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database. The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships. Furthermore, our model predicts ADR pairs that do not exist in the established ADR database, showing its capability to supplement the ADR database.
Image Demoireing with Learnable Bandpass Filters
Authors: Bolun Zheng, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Demoire, Image Enhancement, Image Restoration, Low-Light Image Enhancement, Tone Mapping

Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem. As an end-to-end solution, MBCNN respectively solves the two sub-problems. For texture restoration, we propose a learnable bandpass filter (LBF) to learn the frequency prior for moire texture removal. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, and then performs local fine tuning of the color per pixel. Through an ablation study, we demonstrate the effectiveness of the different components of MBCNN. Experimental results on two public datasets show that our method outperforms state-of-the-art methods by a large margin (more than 2dB in terms of PSNR).
A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection
Authors: Songyan Xue, Yi Ma, Na Yi, Terence E. Dodgson
Date: 2020-04-01 00:00:00
Tasks: Quantization

In this paper, we reveal that artificial neural network (ANN) assisted multiple-input multiple-output (MIMO) signal detection can be modeled as ANN-assisted lossy vector quantization (VQ), named MIMO-VQ, which is basically a joint statistical channel quantization and signal quantization procedure. It is found that the quantization loss increases linearly with the number of transmit antennas, and thus MIMO-VQ scales poorly with the size of MIMO. Motivated by this finding, we propose a novel modular neural network based approach, termed MNNet, where the whole network is formed by a set of pre-defined ANN modules. The key of ANN module design lies in the integration of parallel interference cancellation in the MNNet, which linearly reduces the interference (or equivalently the number of transmit-antennas) along the feed-forward propagation; and so as the quantization loss. Our simulation results show that the MNNet approach largely improves the deep-learning capacity with near-optimal performance in various cases. Provided that MNNet is well modularized, the learning procedure does not need to be applied on the entire network as a whole, but rather at the modular level. Due to this reason, MNNet has the advantage of much lower learning complexity than other deep-learning based MIMO detection approaches.
Two-shot Spatially-varying BRDF and Shape Estimation
Authors: Mark Boss, Varun Jampani, Kihwan Kim, Hendrik P. A. Lensch, Jan Kautz
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Vocal Bursts Valence Prediction

Capturing the shape and spatially-varying appearance (SVBRDF) of an object from images is a challenging task that has applications in both computer vision and graphics. Traditional optimization-based approaches often need a large number of images taken from multiple views in a controlled environment. Newer deep learning-based approaches require only a few input images, but the reconstruction quality is not on par with optimization techniques. We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. The previous predictions guide each estimation, and a joint refinement network later refines both SVBRDF and shape. We follow a practical mobile image capture setting and use unaligned two-shot flash and no-flash images as input. Both our two-shot image capture and network inference can run on mobile hardware. We also create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images. Comparisons with recent approaches demonstrate the superior performance of the proposed approach.
More Grounded Image Captioning by Distilling Image-Text Matching Model
Authors: Yuanen Zhou, Meng Wang, Daqing Liu, Zhenzhen Hu, Hanwang Zhang
Date: 2020-04-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Captioning, Image-text matching, Knowledge Distillation, POS, Sentence, Text Matching

Visual attention not only improves the performance of image captioners, but also serves as a visual interpretation to qualitatively measure the caption rationality and model transparency. Specifically, we expect that a captioner can fix its attentive gaze on the correct objects while generating the corresponding words. This ability is also known as grounded image captioning. However, the grounding accuracy of existing captioners is far from satisfactory. To improve the grounding accuracy while retaining the captioning quality, it is expensive to collect the word-region alignment as strong supervision. To this end, we propose a Part-of-Speech (POS) enhanced image-text matching model (SCAN \cite{lee2018stacked}): POS-SCAN, as the effective knowledge distillation for more grounded image captioning. The benefits are two-fold: 1) given a sentence and an image, POS-SCAN can ground the objects more accurately than SCAN; 2) POS-SCAN serves as a word-region alignment regularization for the captioner's visual attention module. By showing benchmark experimental results, we demonstrate that conventional image captioners equipped with POS-SCAN can significantly improve the grounding accuracy without strong supervision. Last but not the least, we explore the indispensable Self-Critical Sequence Training (SCST) \cite{Rennie_2017_CVPR} in the context of grounded image captioning and show that the image-text matching score can serve as a reward for more grounded captioning \footnote{https://github.com/YuanEZhou/Grounded-Image-Captioning}.