pwc-archive/papers-with-abstracts huggingface dataset:
Safe Fakes: Evaluating Face Anonymizers for Face Detectors
Authors: Sander R. Klomp, Matthew van Rijn, Rob G. J. Wijnhoven, Cees G. M. Snoek, Peter H. N. de With
Date: 2021-04-23 00:00:00
Tasks: Face Detection, Generative Adversarial Network, Privacy Preserving

Since the introduction of the GDPR and CCPA legislation, both public and private facial image datasets are increasingly scrutinized. Several datasets have been taken offline completely and some have been anonymized. However, it is unclear how anonymization impacts face detection performance. To our knowledge, this paper presents the first empirical study on the effect of image anonymization on supervised training of face detectors. We compare conventional face anonymizers with three state-of-the-art Generative Adversarial Network-based (GAN) methods, by training an off-the-shelf face detector on anonymized data. Our experiments investigate the suitability of anonymization methods for maintaining face detector performance, the effect of detectors overtraining on anonymization artefacts, dataset size for training an anonymizer, and the effect of training time of anonymization GANs. A final experiment investigates the correlation between common GAN evaluation metrics and the performance of a trained face detector. Although all tested anonymization methods lower the performance of trained face detectors, faces anonymized using GANs cause far smaller performance degradation than conventional methods. As the most important finding, the best-performing GAN, DeepPrivacy, removes identifiable faces for a face detector trained on anonymized data, resulting in a modest decrease from 91.0 to 88.3 mAP. In the last few years, there have been rapid improvements in realism of GAN-generated faces. We expect that further progression in GAN research will allow the use of Deep Fake technology for privacy-preserving Safe Fakes, without any performance degradation for training face detectors.
Skeletor: Skeletal Transformers for Robust Body-Pose Estimation
Authors: Tao Jiang, Necati Cihan Camgoz, Richard Bowden
Date: 2021-04-23 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation, Sign Language Translation, Translation

Predicting 3D human pose from a single monoscopic video can be highly challenging due to factors such as low resolution, motion blur and occlusion, in addition to the fundamental ambiguity in estimating 3D from 2D. Approaches that directly regress the 3D pose from independent images can be particularly susceptible to these factors and result in jitter, noise and/or inconsistencies in skeletal estimation. Much of which can be overcome if the temporal evolution of the scene and skeleton are taken into account. However, rather than tracking body parts and trying to temporally smooth them, we propose a novel transformer based network that can learn a distribution over both pose and motion in an unsupervised fashion. We call our approach Skeletor. Skeletor overcomes inaccuracies in detection and corrects partial or entire skeleton corruption. Skeletor uses strong priors learn from on 25 million frames to correct skeleton sequences smoothly and consistently. Skeletor can achieve this as it implicitly learns the spatio-temporal context of human motion via a transformer based neural network. Extensive experiments show that Skeletor achieves improved performance on 3D human pose estimation and further provides benefits for downstream tasks such as sign language translation.
MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance
Authors: Roberto Olmos, Siham Tabik, Francisco Perez-Hernandez, Alberto Lamas, Francisco Herrera
Date: 2021-04-23 00:00:00

Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable. Using such detectors still implies a high number of false alarms and false negatives. In this context, most existent studies select one of the latest single-image detectors and train it on a better dataset or use some pre-processing, post-processing or data-fusion approach to further reduce false alarms. However, none of these works tried to exploit the temporal information present in the videos to mitigate false detections. This paper presents a new system, called MULTI Confirmation-level Alarm SysTem based on Convolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM) (MULTICAST), that leverages not only the spacial information but also the temporal information existent in the videos for a more reliable handgun detection. MULTICAST consists of three stages, i) a handgun detection stage, ii) a CNN-based spacial confirmation stage and iii) LSTM-based temporal confirmation stage. The temporal confirmation stage uses the positions of the detected handgun in previous instants to predict its trajectory in the next frame. Our experiments show that MULTICAST reduces by 80% the number of false alarms with respect to Faster R-CNN based-single-image detector, which makes it more useful in providing more effective and rapid security responses.
GuideBP: Guiding Backpropagation Through Weaker Pathways of Parallel Logits
Authors: Bodhisatwa Mandal, Swarnendu Ghosh, Teresa Gonçalves, Paulo Quaresma, Mita Nasipuri, Nibaran Das
Date: 2021-04-23 00:00:00
Tasks: Class-Specific Performance

Convolutional neural networks often generate multiple logits and use simple techniques like addition or averaging for loss computation. But this allows gradients to be distributed equally among all paths. The proposed approach guides the gradients of backpropagation along weakest concept representations. A weakness scores defines the class specific performance of individual pathways which is then used to create a logit that would guide gradients along the weakest pathways. The proposed approach has been shown to perform better than traditional column merging techniques and can be used in several application scenarios. Not only can the proposed model be used as an efficient technique for training multiple instances of a model parallely, but also CNNs with multiple output branches have been shown to perform better with the proposed upgrade. Various experiments establish the flexibility of the learning technique which is simple yet effective in various multi-objective scenarios both empirically and statistically.
Co-training for Deep Object Detection: Comparing Single-modal and Multi-modal Approaches
Authors: Jose L. Gómez, Gabriel Villalonga, Antonio M. López
Date: 2021-04-23 00:00:00
Tasks: Depth Estimation, object-detection, Object Detection

Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on pair; at least, when using an off-the-shelf depth estimation model not specifically trained on the translated images.
Region-Adaptive Deformable Network for Image Quality Assessment
Authors: Shuwei Shi, Qingyan Bai, Mingdeng Cao, Weihao Xia, Jiahao Wang, Yifan Chen, Yujiu Yang
Date: 2021-04-23 00:00:00
Tasks: Image Quality Assessment, Image Restoration

Image quality assessment (IQA) aims to assess the perceptual quality of images. The outputs of the IQA algorithms are expected to be consistent with human subjective perception. In image restoration and enhancement tasks, images generated by generative adversarial networks (GAN) can achieve better visual performance than traditional CNN-generated images, although they have spatial shift and texture noise. Unfortunately, the existing IQA methods have unsatisfactory performance on the GAN-based distortion partially because of their low tolerance to spatial misalignment. To this end, we propose the reference-oriented deformable convolution, which can improve the performance of an IQA network on GAN-based distortion by adaptively considering this misalignment. We further propose a patch-level attention module to enhance the interaction among different patch regions, which are processed independently in previous patch-based methods. The modified residual block is also proposed by applying modifications to the classic residual block to construct a patch-region-based baseline called WResNet. Equipping this baseline with the two proposed modules, we further propose Region-Adaptive Deformable Network (RADN). The experiment results on the NTIRE 2021 Perceptual Image Quality Assessment Challenge dataset show the superior performance of RADN, and the ensemble approach won fourth place in the final testing phase of the challenge. Code is available at https://github.com/IIGROUP/RADN.
STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains
Authors: Fabian Gröger, Anne-Marie Rickmann, Christian Wachinger
Date: 2021-04-23 00:00:00
Tasks: Domain Adaptation, Pseudo Label, Segmentation, Unsupervised Domain Adaptation

We propose an unsupervised domain adaptation (UDA) approach for white matter hyperintensity (WMH) segmentation, which uses Self-Training with Uncertainty DEpendent Label refinement (STRUDEL). Self-training has recently been introduced as a highly effective method for UDA, which is based on self-generated pseudo labels. However, pseudo labels can be very noisy and therefore deteriorate model performance. We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty. STRUDEL is further improved by incorporating the segmentation output of an existing method in the pseudo label generation that showed high robustness for WMH segmentation. In our experiments, we evaluate STRUDEL with a standard U-Net and a modified network with a higher receptive field. Our results on WMH segmentation across datasets demonstrate the significant improvement of STRUDEL with respect to standard self-training.
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking
Authors: Ziyi Cheng, Xuhong Ren, Felix Juefei-Xu, Wanli Xue, Qing Guo, Lei Ma, Jianjun Zhao
Date: 2021-04-23 00:00:00
Tasks: Data Augmentation, Object, Object Tracking, Visual Object Tracking

Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking. Recent works mainly focus on constructing effective and efficient updating methods while neglecting the training samples for learning discriminative object models, which is also a key part of a learning problem. In this paper, we propose the DeepMix that takes historical samples' embeddings as input and generates augmented embeddings online, enhancing the state-of-the-art online learning methods for visual object tracking. More specifically, we first propose the online data augmentation for tracking that online augments the historical samples through object-aware filtering. Then, we propose MixNet which is an offline trained network for performing online data augmentation within one-step, enhancing the tracking accuracy while preserving high speeds of the state-of-the-art online learning methods. The extensive experiments on three different tracking frameworks, i.e., DiMP, DSiam, and SiamRPN++, and three large-scale and challenging datasets, \ie, OTB-2015, LaSOT, and VOT, demonstrate the effectiveness and advantages of the proposed method.
CapillaryNet: An Automated System to Quantify Skin Capillary Density and Red Blood Cell Velocity from Handheld Vital Microscopy
Authors: Maged Helmy, Anastasiya Dykyy, Tuyen Trung Truong, Paulo Ferreira, Eric Jul
Date: 2021-04-23 00:00:00

Capillaries are the smallest vessels in the body responsible for delivering oxygen and nutrients to surrounding cells. Various life-threatening diseases are known to alter the density of healthy capillaries and the flow velocity of erythrocytes within the capillaries. In previous studies, capillary density and flow velocity were manually assessed by trained specialists. However, manual analysis of a standard 20-second microvascular video requires 20 minutes on average and necessitates extensive training. Thus, manual analysis has been reported to hinder the application of microvascular microscopy in a clinical environment. To address this problem, this paper presents a fully automated state-of-the-art system to quantify skin nutritive capillary density and red blood cell velocity captured by handheld-based microscopy videos. The proposed method combines the speed of traditional computer vision algorithms with the accuracy of convolutional neural networks to enable clinical capillary analysis. The results show that the proposed system fully automates capillary detection with an accuracy exceeding that of trained analysts and measures several novel microvascular parameters that had eluded quantification thus far, namely, capillary hematocrit and intracapillary flow velocity heterogeneity. The proposed end-to-end system, named CapillaryNet, can detect capillaries at $\sim$0.9 seconds per frame with $\sim$93\% accuracy. The system is currently being used as a clinical research product in a larger e-health application to analyse capillary data captured from patients suffering from COVID-19, pancreatitis, and acute heart diseases. CapillaryNet narrows the gap between the analysis of microcirculation images in a clinical environment and state-of-the-art systems.
AttWalk: Attentive Cross-Walks for Deep Mesh Analysis
Authors: Ran Ben Izhak, Alon Lahav, Ayellet Tal
Date: 2021-04-23 00:00:00
Tasks: Retrieval

Mesh representation by random walks has been shown to benefit deep learning. Randomness is indeed a powerful concept. However, it comes with a price: some walks might wander around non-characteristic regions of the mesh, which might be harmful to shape analysis, especially when only a few walks are utilized. We propose a novel walk-attention mechanism that leverages the fact that multiple walks are used. The key idea is that the walks may provide each other with information regarding the meaningful (attentive) features of the mesh. We utilize this mutual information to extract a single descriptor of the mesh. This differs from common attention mechanisms that use attention to improve the representation of each individual descriptor. Our approach achieves SOTA results for two basic 3D shape analysis tasks: classification and retrieval. Even a handful of walks along a mesh suffice for learning.
The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability System
Authors: Lorin Sweeney, Graham Healy, Alan F. Smeaton
Date: 2021-04-23 00:00:00
Tasks: Multimodal Deep Learning, Video Recognition

Memories are the tethering threads that tie us to the world, and memorability is the measure of their tensile strength. The threads of memory are spun from fibres of many modalities, obscuring the contribution of a single fibre to a thread's overall tensile strength. Unfurling these fibres is the key to understanding the nature of their interaction, and how we can ultimately create more meaningful media content. In this paper, we examine the influence of audio on video recognition memorability, finding evidence to suggest that it can facilitate overall video recognition memorability rich in high-level (gestalt) audio features. We introduce a novel multimodal deep learning-based late-fusion system that uses audio gestalt to estimate the influence of a given video's audio on its overall short-term recognition memorability, and selectively leverages audio features to make a prediction accordingly. We benchmark our audio gestalt based system on the Memento10k short-term video memorability dataset, achieving top-2 state-of-the-art results.
Weakly-supervised Multi-task Learning for Multimodal Affect Recognition
Authors: Wenliang Dai, Samuel Cahyawijaya, Yejin Bang, Pascale Fung
Date: 2021-04-23 00:00:00
Tasks: Emotion Recognition, Multi-Task Learning, Sentiment Analysis

Multimodal affect recognition constitutes an important aspect for enhancing interpersonal relationships in human-computer interaction. However, relevant data is hard to come by and notably costly to annotate, which poses a challenging barrier to build robust multimodal affect recognition systems. Models trained on these relatively small datasets tend to overfit and the improvement gained by using complex state-of-the-art models is marginal compared to simple baselines. Meanwhile, there are many different multimodal affect recognition datasets, though each may be small. In this paper, we propose to leverage these datasets using weakly-supervised multi-task learning to improve the generalization performance on each of them. Specifically, we explore three multimodal affect recognition tasks: 1) emotion recognition; 2) sentiment analysis; and 3) sarcasm recognition. Our experimental results show that multi-tasking can benefit all these tasks, achieving an improvement up to 2.9% accuracy and 3.3% F1-score. Furthermore, our method also helps to improve the stability of model performance. In addition, our analysis suggests that weak supervision can provide a comparable contribution to strong supervision if the tasks are highly correlated.
Sketch-based Normal Map Generation with Geometric Sampling
Authors: Yi He, Haoran Xie, Chao Zhang, Xi Yang, Kazunori Miyata
Date: 2021-04-23 00:00:00
Tasks: Generative Adversarial Network

Normal map is an important and efficient way to represent complex 3D models. A designer may benefit from the auto-generation of high quality and accurate normal maps from freehand sketches in 3D content creation. This paper proposes a deep generative model for generating normal maps from users sketch with geometric sampling. Our generative model is based on Conditional Generative Adversarial Network with the curvature-sensitive points sampling of conditional masks. This sampling process can help eliminate the ambiguity of generation results as network input. In addition, we adopted a U-Net structure discriminator to help the generator be better trained. It is verified that the proposed framework can generate more accurate normal maps.
Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid
Authors: Mengfan Li
Date: 2021-04-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and labor-intensive and difficulty in extracting high-quality features. Therefore, the paper proposes a computer-based feature fusion Convolutional neural network breast cancer image classification and detection method. The paper pre-trains two convolutional neural networks with different structures, and then uses the convolutional neural network to automatically extract the characteristics of features, fuse the features extracted from the two structures, and finally use the classifier classifies the fused features. The experimental results show that the accuracy of this method in the classification of breast cancer image data sets is 89%, and the classification accuracy of breast cancer images is significantly improved compared with traditional methods.
Fine-Grained Texture Identification for Reliable Product Traceability
Authors: Junsong Wang, Yubo Li, Zhiyong Chang, Haitao Yue, Yonghua Lin
Date: 2021-04-23 00:00:00

Texture exists in lots of the products, such as wood, beef and compression tea. These abundant and stochastic texture patterns are significantly different between any two products. Unlike the traditional digital ID tracking, in this paper, we propose a novel approach for product traceability, which directly uses the natural texture of the product itself as the unique identifier. A texture identification based traceability system for Pu'er compression tea is developed to demonstrate the feasibility of the proposed solution. With tea-brick images collected from manufactures and individual users, a large-scale dataset has been formed to evaluate the performance of tea-brick texture verification and searching algorithm. The texture similarity approach with local feature extraction and matching achieves the verification accuracy of 99.6% and the top-1 searching accuracy of 98.9%, respectively.
Middle-level Fusion for Lightweight RGB-D Salient Object Detection
Authors: Nianchang Huang, Qiang Zhang, Jungong Han
Date: 2021-04-23 00:00:00
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, Salient Object Detection

Most existing lightweight RGB-D salient object detection (SOD) models are based on two-stream structure or single-stream structure. The former one first uses two sub-networks to extract unimodal features from RGB and depth images, respectively, and then fuses them for SOD. While, the latter one directly extracts multi-modal features from the input RGB-D images and then focuses on exploiting cross-level complementary information. However, two-stream structure based models inevitably require more parameters and single-stream structure based ones cannot well exploit the cross-modal complementary information since they ignore the modality difference. To address these issues, we propose to employ the middle-level fusion structure for designing lightweight RGB-D SOD model in this paper, which first employs two sub-networks to extract low- and middle-level unimodal features, respectively, and then fuses those extracted middle-level unimodal features for extracting corresponding high-level multi-modal features in the subsequent sub-network. Different from existing models, this structure can effectively exploit the cross-modal complementary information and significantly reduce the network's parameters, simultaneously. Therefore, a novel lightweight SOD model is designed, which contains a information-aware multi-modal feature fusion (IMFF) module for effectively capturing the cross-modal complementary information and a lightweight feature-level and decision-level feature fusion (LFDF) module for aggregating the feature-level and the decision-level saliency information in different stages with less parameters. Our proposed model has only 3.9M parameters and runs at 33 FPS. The experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed method over some state-of-the-art methods.