pwc-archive/papers-with-abstracts huggingface dataset:
IronMask: Modular Architecture for Protecting Deep Face Template
Authors: Sunpill Kim, Yunseong Jeong, Jinsu Kim, Jungkon Kim, Hyung Tae Lee, Jae Hong Seo
Date: 2021-04-06 00:00:00
Proceeding: CVPR 2021 1
Tasks: Binarization, Face Recognition, TAR

Convolutional neural networks have made remarkable progress in the face recognition field. The more the technology of face recognition advances, the greater discriminative features into a face template. However, this increases the threat to user privacy in case the template is exposed. In this paper, we present a modular architecture for face template protection, called IronMask, that can be combined with any face recognition system using angular distance metric. We circumvent the need for binarization, which is the main cause of performance degradation in most existing face template protections, by proposing a new real-valued error-correcting-code that is compatible with real-valued templates and can therefore, minimize performance degradation. We evaluate the efficacy of IronMask by extensive experiments on two face recognitions, ArcFace and CosFace with three datasets, CMU-Multi-PIE, FEI, and Color-FERET. According to our experimental results, IronMask achieves a true accept rate (TAR) of 99.79% at a false accept rate (FAR) of 0.0005% when combined with ArcFace, and 95.78% TAR at 0% FAR with CosFace, while providing at least 115-bit security against known attacks.
Hippocampus-heuristic Character Recognition Network for Zero-shot Learning
Authors: Shaowei Wang, Guanjie Huang, Xiangyu Luo
Date: 2021-04-06 00:00:00
Tasks: Hippocampus, Zero-Shot Learning

The recognition of Chinese characters has always been a challenging task due to their huge variety and complex structures. The latest research proves that such an enormous character set can be decomposed into a collection of about 500 fundamental Chinese radicals, and based on which this problem can be solved effectively. While with the constant advent of novel Chinese characters, the number of basic radicals is also expanding. The current methods that entirely rely on existing radicals are not flexible for identifying these novel characters and fail to recognize these Chinese characters without learning all of their radicals in the training stage. To this end, this paper proposes a novel Hippocampus-heuristic Character Recognition Network (HCRN), which references the way of hippocampus thinking, and can recognize unseen Chinese characters (namely zero-shot learning) only by training part of radicals. More specifically, the network architecture of HCRN is a new pseudo-siamese network designed by us, which can learn features from pairs of input training character samples and use them to predict unseen Chinese characters. The experimental results show that HCRN is robust and effective. It can accurately predict about 16,330 unseen testing Chinese characters relied on only 500 trained Chinese characters. The recognition accuracy of HCRN outperforms the state-of-the-art Chinese radical recognition approach by 15% (from 85.1% to 99.9%) for recognizing unseen Chinese characters.
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning
Authors: Yang Chen, Pinhao Song, Hong Liu, Linhui Dai, Xiaochuan Zhang, Runwei Ding, Shengquan Li
Date: 2021-04-06 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Domain Generalization, Image Stylization, object-detection, Object Detection, Object Recognition, Style Transfer

The performance of existing underwater object detection methods degrades seriously when facing domain shift caused by complicated underwater environments. Due to the limitation of the number of domains in the dataset, deep detectors easily memorize a few seen domains, which leads to low generalization ability. There are two common ideas to improve the domain generalization performance. First, it can be inferred that the detector trained on as many domains as possible is domain-invariant. Second, for the images with the same semantic content in different domains, their hidden features should be equivalent. This paper further excavates these two ideas and proposes a domain generalization framework (named DMC) that learns how to generalize across domains from Domain Mixup and Contrastive Learning. First, based on the formation of underwater images, an image in an underwater environment is the linear transformation of another underwater environment. Thus, a style transfer model, which outputs a linear transformation matrix instead of the whole image, is proposed to transform images from one source domain to another, enriching the domain diversity of the training data. Second, mixup operation interpolates different domains on the feature level, sampling new domains on the domain manifold. Third, contrastive loss is selectively applied to features from different domains to force the model to learn domain invariant features but retain the discriminative capacity. With our method, detectors will be robust to domain shift. Also, a domain generalization benchmark S-UODAC2020 for detection is set up to measure the performance of our method. Comprehensive experiments on S-UODAC2020 and two object recognition benchmarks (PACS and VLCS) demonstrate that the proposed method is able to learn domain-invariant representations, and outperforms other domain generalization methods.
Insight about Detection, Prediction and Weather Impact of Coronavirus (Covid-19) using Neural Network
Authors: A K M Bahalul Haque, Tahmid Hasan Pranto, Abdulla All Noman, Atik Mahmood
Date: 2021-04-05 00:00:00

The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16, VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the LogisticRegression, which can predict death of an individual with a model accuracy of 94.40%.
Learning Optical Flow from a Few Matches
Authors: Shihao Jiang, Yao Lu, Hongdong Li, Richard Hartley
Date: 2021-04-05 00:00:00
Proceeding: CVPR 2021 1
Tasks: Optical Flow Estimation

State-of-the-art neural network models for optical flow estimation require a dense correlation volume at high resolutions for representing per-pixel displacement. Although the dense correlation volume is informative for accurate estimation, its heavy computation and memory usage hinders the efficient training and deployment of the models. In this paper, we show that the dense correlation volume representation is redundant and accurate flow estimation can be achieved with only a fraction of elements in it. Based on this observation, we propose an alternative displacement representation, named Sparse Correlation Volume, which is constructed directly by computing the k closest matches in one feature map for each feature vector in the other feature map and stored in a sparse data structure. Experiments show that our method can reduce computational cost and memory use significantly, while maintaining high accuracy compared to previous approaches with dense correlation volumes. Code is available at https://github.com/zacjiang/scv .
Unified Detection of Digital and Physical Face Attacks
Authors: Debayan Deb, Xiaoming Liu, Anil K. Jain
Date: 2021-04-05 00:00:00
Tasks: Clustering, Multi-Task Learning

State-of-the-art defense mechanisms against face attacks achieve near perfect accuracies within one of three attack categories, namely adversarial, digital manipulation, or physical spoofs, however, they fail to generalize well when tested across all three categories. Poor generalization can be attributed to learning incoherent attacks jointly. To overcome this shortcoming, we propose a unified attack detection framework, namely UniFAD, that can automatically cluster 25 coherent attack types belonging to the three categories. Using a multi-task learning framework along with k-means clustering, UniFAD learns joint representations for coherent attacks, while uncorrelated attack types are learned separately. Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94.73% @ 0.2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories. Proposed method can detect an attack within 3 milliseconds on a Nvidia 2080Ti. UniFAD can also identify the attack types and categories with 75.81% and 97.37% accuracies, respectively.
Automatic Micro-Expression Apex Frame Spotting using Local Binary Pattern from Six Intersection Planes
Authors: Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi
Date: 2021-04-05 00:00:00

Facial expressions are one of the most effective ways for non-verbal communications, which can be expressed as the Micro-Expression (ME) in the high-stake situations. The MEs are involuntary, rapid, and, subtle, and they can reveal real human intentions. However, their feature extraction is very challenging due to their low intensity and very short duration. Although Local Binary Pattern from Three Orthogonal Plane (LBP-TOP) feature extractor is useful for the ME analysis, it does not consider essential information. To address this problem, we propose a new feature extractor called Local Binary Pattern from Six Intersection Planes (LBP-SIPl). This method extracts LBP code on six intersection planes, and then it combines them. Results show that the proposed method has superior performance in apex frame spotting automatically in comparison with the relevant methods on the CASME database. Simulation results show that, using the proposed method, the apex frame has been spotted in 43% of subjects in the CASME database, automatically. Also, the mean absolute error of 1.76 is achieved, using our novel proposed method.
Action Shuffle Alternating Learning for Unsupervised Action Segmentation
Authors: Jun Li, Sinisa Todorovic
Date: 2021-04-05 00:00:00
Proceeding: CVPR 2021 1
Tasks: Action Segmentation, Segmentation, Self-Supervised Learning, Unsupervised Action Segmentation

This paper addresses unsupervised action segmentation. Prior work captures the frame-level temporal structure of videos by a feature embedding that encodes time locations of frames in the video. We advance prior work with a new self-supervised learning (SSL) of a feature embedding that accounts for both frame- and action-level structure of videos. Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding. The positive and negative sequences consist of action segments sampled from videos, where in the former the sampled action segments respect their time ordering in the video, and in the latter they are shuffled. As supervision of actions is not available and our SSL requires access to action segments, we specify an HMM that explicitly models action lengths, and infer a MAP action segmentation with the Viterbi algorithm. The resulting action segmentation is used as pseudo-ground truth for estimating our action-level feature embedding and updating the HMM. We alternate the above steps within the Generalized EM framework, which ensures convergence. Our evaluation on the Breakfast, YouTube Instructions, and 50Salads datasets gives superior results to those of the state of the art.
Anchor-Constrained Viterbi for Set-Supervised Action Segmentation
Authors: Jun Li, Sinisa Todorovic
Date: 2021-04-05 00:00:00
Proceeding: CVPR 2021 1
Tasks: Action Segmentation, Segmentation, Temporal Sequences

This paper is about action segmentation under weak supervision in training, where the ground truth provides only a set of actions present, but neither their temporal ordering nor when they occur in a training video. We use a Hidden Markov Model (HMM) grounded on a multilayer perceptron (MLP) to label video frames, and thus generate a pseudo-ground truth for the subsequent pseudo-supervised training. In testing, a Monte Carlo sampling of action sets seen in training is used to generate candidate temporal sequences of actions, and select the maximum posterior sequence. Our key contribution is a new anchor-constrained Viterbi algorithm (ACV) for generating the pseudo-ground truth, where anchors are salient action parts estimated for each action from a given ground-truth set. Our evaluation on the tasks of action segmentation and alignment on the benchmark Breakfast, MPII Cooking2, Hollywood Extended datasets demonstrates our superior performance relative to that of prior work.
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models
Authors: Neal Mangaokar, Jiameng Pu, Parantapa Bhattacharya, Chandan K. Reddy, Bimal Viswanath
Date: 2021-04-05 00:00:00
Tasks: Style Transfer, Translation

Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a patient and translates it to a new image that indicates an attacker-chosen disease condition. The potential for fraudulent claims based on such generated 'fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities. We show that these attacks manage to mislead both medical professionals and algorithmic detection schemes. Lastly, we also investigate defensive measures based on machine learning to detect images generated by Jekyll.
Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models
Authors: Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger, Icxel Valeriano Quiroz, Nikolaus Pinger, Soroosh Baselizadeh, Seong Tae Kim, Nassir Navab
Date: 2021-04-04 00:00:00
Tasks: COVID-19 Diagnosis, Prediction

Convolutional neural networks are showing promise in the automatic diagnosis of thoracic pathologies on chest x-rays. Their black-box nature has sparked many recent works to explain the prediction via input feature attribution methods (aka saliency methods). However, input feature attribution methods merely identify the importance of input regions for the prediction and lack semantic interpretation of model behavior. In this work, we first identify the semantics associated with internal units (feature maps) of the network. We proceed to investigate the following questions; Does a regression model that is only trained with COVID-19 severity scores implicitly learn visual patterns associated with thoracic pathologies? Does a network that is trained on weakly labeled data (e.g. healthy, unhealthy) implicitly learn pathologies? Moreover, we investigate the effect of pretraining and data imbalance on the interpretability of learned features. In addition to the analysis, we propose semantic attribution to semantically explain each prediction. We present our findings using publicly available chest pathologies (CheXpert, NIH ChestX-ray8) and COVID-19 datasets (BrixIA, and COVID-19 chest X-ray segmentation dataset). The Code is publicly available.
A Modified Convolutional Network for Auto-encoding based on Pattern Theory Growth Function
Authors: Erico Tjoa
Date: 2021-04-04 00:00:00

This brief paper reports the shortcoming of a variant of convolutional neural network whose components are developed based on the pattern theory framework.
Mutual Graph Learning for Camouflaged Object Detection
Authors: Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, Deng-Ping Fan
Date: 2021-04-03 00:00:00
Proceeding: CVPR 2021 1
Tasks: Graph Learning, Object, object-detection, Object Detection

Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features extracted by deep model indistinguishable. To overcome this challenge, an ideal model should be able to seek valuable, extra clues from the given scene and incorporate them into a joint learning framework for representation co-enhancement. With this inspiration, we design a novel Mutual Graph Learning (MGL) model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. Specifically, MGL decouples an image into two task-specific feature maps -- one for roughly locating the target and the other for accurately capturing its boundary details -- and fully exploits the mutual benefits by recurrently reasoning their high-order relations through graphs. Importantly, in contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations to maximize information interactions. Experiments on challenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods.
Optical Flow Dataset Synthesis from Unpaired Images
Authors: Adrian Wälchli, Paolo Favaro
Date: 2021-04-02 00:00:00
Tasks: Optical Flow Estimation

The estimation of optical flow is an ambiguous task due to the lack of correspondence at occlusions, shadows, reflections, lack of texture and changes in illumination over time. Thus, unsupervised methods face major challenges as they need to tune complex cost functions with several terms designed to handle each of these sources of ambiguity. In contrast, supervised methods avoid these challenges altogether by relying on explicit ground truth optical flow obtained directly from synthetic or real data. In the case of synthetic data, the ground truth provides an exact and explicit description of what optical flow to assign to a given scene. However, the domain gap between synthetic data and real data often limits the ability of a trained network to generalize. In the case of real data, the ground truth is obtained through multiple sensors and additional data processing, which might introduce persistent errors and contaminate it. As a solution to these issues, we introduce a novel method to build a training set of pseudo-real images that can be used to train optical flow in a supervised manner. Our dataset uses two unpaired frames from real data and creates pairs of frames by simulating random warps, occlusions with super-pixels, shadows and illumination changes, and associates them to their corresponding exact optical flow. We thus obtain the benefit of directly training on real data while having access to an exact ground truth. Training with our datasets on the Sintel and KITTI benchmarks is straightforward and yields models on par or with state of the art performance compared to much more sophisticated training approaches.
Malignancy Prediction and Lesion Identification from Clinical Dermatological Images
Authors: Meng Xia, Meenal K. Kheterpal, Samantha C. Wong, Christine Park, William Ratliff, Lawrence Carin, Ricardo Henao
Date: 2021-04-02 00:00:00
Tasks: Prediction

We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy that can be used for high-level screening processes. Further, we consider augmenting the proposed approach with clinical covariates (from electronic health records) and publicly available data (the ISIC dataset). Comprehensive experiments validated on an independent test dataset demonstrate that i) the proposed approach outperforms alternative model architectures; ii) the model based on images outperforms a pure clinical model by a large margin, and the combination of images and clinical data does not significantly improves over the image-only model; and iii) the proposed framework offers comparable performance in terms of malignancy classification relative to three board certified dermatologists with different levels of experience.
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study
Authors: Miguel Rodríguez Santander, Juan Hernández Albarracín, Adín Ramírez Rivera
Date: 2021-04-02 00:00:00
Tasks: Data Augmentation, Deep Learning, Facial Expression Recognition, Facial Expression Recognition (FER), Transfer Learning, Video Classification, Video Recognition

Deep learning models need large amounts of data for training. In video recognition and classification, significant advances were achieved with the introduction of new large databases. However, the creation of large-databases for training is infeasible in several scenarios. Thus, existing or small collected databases are typically joined and amplified to train these models. Nevertheless, training neural networks on limited data is not straightforward and comes with a set of problems. In this paper, we explore the effects of stacking databases, model initialization, and data amplification techniques when training with limited data on deep learning models' performance. We focused on the problem of Facial Expression Recognition from videos. We performed an extensive study with four databases at a different complexity and nine deep-learning architectures for video classification. We found that (i) complex training sets translate better to more stable test sets when trained with transfer learning and synthetically generated data, but their performance yields a high variance; (ii) training with more detailed data translates to more stable performance on novel scenarios (albeit with lower performance); (iii) merging heterogeneous data is not a straightforward improvement, as the type of augmentation and initialization is crucial; (iv) classical data augmentation cannot fill the holes created by joining largely separated datasets; and (v) inductive biases help to bridge the gap when paired with synthetic data, but this data is not enough when working with standard initialization techniques.