pwc-archive/papers-with-abstracts huggingface dataset:
Enabling ISP-less Low-Power Computer Vision
Authors: Gourav Datta, Zeyu Liu, Zihan Yin, Linyu Sun, Akhilesh R. Jaiswal, Peter A. Beerel
Date: 2022-10-11 00:00:00
Tasks: Demosaicking, Few-Shot Learning

In order to deploy current computer vision (CV) models on resource-constrained low-power devices, recent works have proposed in-sensor and in-pixel computing approaches that try to partly/fully bypass the image signal processor (ISP) and yield significant bandwidth reduction between the image sensor and the CV processing unit by downsampling the activation maps in the initial convolutional neural network (CNN) layers. However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISP-processed images used for training. Moreover, it is difficult to train deep CV models on raw images, because most (if not all) large-scale open-source datasets consist of RGB images. To mitigate this concern, we propose to invert the ISP pipeline, which can convert the RGB images of any dataset to its raw counterparts, and enable model training on raw images. We release the raw version of the COCO dataset, a large-scale benchmark for generic high-level vision tasks. For ISP-less CV systems, training on these raw images result in a 7.1% increase in test accuracy on the visual wake works (VWW) dataset compared to relying on training with traditional ISP-processed RGB datasets. To further improve the accuracy of ISP-less CV models and to increase the energy and bandwidth benefits obtained by in-sensor/in-pixel computing, we propose an energy-efficient form of analog in-pixel demosaicing that may be coupled with in-pixel CNN computations. When evaluated on raw images captured by real sensors from the PASCALRAW dataset, our approach results in a 8.1% increase in mAP. Lastly, we demonstrate a further 20.5% increase in mAP by using a novel application of few-shot learning with thirty shots each for the novel PASCALRAW dataset, constituting 3 classes.
CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis
Authors: Wojciech Prazuch, Aleksandra Suwalska, Marek Socha, Joanna Tobiasz, Pawel Foszner, Jerzy Jaroszewicz, Katarzyna Gruszczynska, Magdalena Sliwinska, Jerzy Walecki, Tadeusz Popiela, Grzegorz Przybylski, Andrzej Cieszanowski, Mateusz Nowak, Malgorzata Pawlowska, Robert Flisiak, Krzysztof Simon, Gabriela Zapolska, Barbara Gizycka, Edyta Szurowska, POLCOVID Study Group, Michal Marczyk, Joanna Polanska
Date: 2022-10-11 00:00:00
Tasks: COVID-19 Diagnosis, Specificity

Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. Regardless of the numerous proposed solutions for lung X-rays, none of them is a product that can be used in the clinic. Five different datasets (POLCOVID, AIforCOVID, COVIDx, NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training; 1 050 images were used as a hold-out test set, and 44 247 as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR. Each image class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 network based on processed CXRs and a dense neural network on radiomic features. The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity and NPV than PPV. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1. A similar performance was obtained on the independent dataset for normal and COVID-19 class subtypes. Seventy-six percent of COVID-19 patients wrongly classified as normal cases were annotated by radiologists as with no signs of disease. Finally, we developed the online service (https://circa.aei.polsl.pl) to provide access to fast diagnosis support tools.
Parallel Augmentation and Dual Enhancement for Occluded Person Re-identification
Authors: Zi Wang, Huaibo Huang, Aihua Zheng, Chenglong Li, Ran He
Date: 2022-10-11 00:00:00
Tasks: Occluded Person Re-Identification, Person Re-Identification

Occluded person re-identification (Re-ID), the task of searching for the same person's images in occluded environments, has attracted lots of attention in the past decades. Recent approaches concentrate on improving performance on occluded data by data/feature augmentation or using extra models to predict occlusions. However, they ignore the imbalance problem in this task and can not fully utilize the information from the training data. To alleviate these two issues, we propose a simple yet effective method with Parallel Augmentation and Dual Enhancement (PADE), which is robust on both occluded and non-occluded data and does not require any auxiliary clues. First, we design a parallel augmentation mechanism (PAM) to generate more suitable occluded data to mitigate the negative effects of unbalanced data. Second, we propose the global and local dual enhancement strategy (DES) to promote the context information and details. Experimental results on three widely used occluded datasets and two non-occluded datasets validate the effectiveness of our method. The code is available at https://github.com/littleprince1121/PADE_Parallel_Augmentation_and_Dual_Enhancement_for_Occluded_Person_ReID
DPANET:Dual Pooling Attention Network for Semantic Segmentation
Authors: Dongwei Sun, Zhuolin Gao
Date: 2022-10-11 00:00:00
Tasks: GPU, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is a historic and significant computer vision task. With the help of deep learning techniques, image semantic segmentation has made great progresses. Over recent years, based on guidance of attention mechanism compared with CNN which overcomes the problems of lacking of interaction between different channels, and effective capturing and aggregating contextual information. However, the massive operations generated by the attention mechanism lead to its extremely high complexity and high demand for GPU memory. For this purpose, we propose a lightweight and flexible neural network named Dual Pool Attention Network(DPANet). The most important is that all modules in DPANet generate \textbf{0} parameters. The first component is spatial pool attention module, we formulate an easy and powerful method densely to extract contextual characteristics and reduce the amount of calculation and complexity dramatically.Meanwhile, it demonstrates the power of even and large kernel size. The second component is channel pool attention module. It is known that the computation process of CNN incorporates the information of spatial and channel dimensions. So, the aim of this module is stripping them out, in order to construct relationship of all channels and heighten different channels semantic information selectively. Moreover, we experiments on segmentation datasets, which shows our method simple and effective with low parameters and calculation complexity.
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework
Authors: Tingting Wu, Wenna Wu, Ying Yang, Feng-Lei Fan, Tieyong Zeng
Date: 2022-10-11 00:00:00
Tasks: Deep Learning, Denoising, Image Enhancement, Low-Light Image Enhancement

The Retinex model is one of the most representative and effective methods for low-light image enhancement. However, the Retinex model does not explicitly tackle the noise problem, and shows unsatisfactory enhancing results. In recent years, due to the excellent performance, deep learning models have been widely used in low-light image enhancement. However, these methods have two limitations: i) The desirable performance can only be achieved by deep learning when a large number of labeled data are available. However, it is not easy to curate massive low/normal-light paired data; ii) Deep learning is notoriously a black-box model [1]. It is difficult to explain their inner-working mechanism and understand their behaviors. In this paper, using a sequential Retinex decomposition strategy, we design a plug-and-play framework based on the Retinex theory for simultaneously image enhancement and noise removal. Meanwhile, we develop a convolutional neural network-based (CNN-based) denoiser into our proposed plug-and-play framework to generate a reflectance component. The final enhanced image is produced by integrating the illumination and reflectance with gamma correction. The proposed plug-and-play framework can facilitate both post hoc and ad hoc interpretability. Extensive experiments on different datasets demonstrate that our framework outcompetes the state-of-the-art methods in both image enhancement and denoising.
Learning to Locate Visual Answer in Video Corpus Using Question
Authors: Bin Li, Yixuan Weng, Bin Sun, Shutao Li
Date: 2022-10-11 00:00:00
Tasks: Contrastive Learning, Language Modelling, Retrieval, Video Retrieval

We introduce a new task, named video corpus visual answer localization (VCVAL), which aims to locate the visual answer in a large collection of untrimmed instructional videos using a natural language question. This task requires a range of skills - the interaction between vision and language, video retrieval, passage comprehension, and visual answer localization. In this paper, we propose a cross-modal contrastive global-span (CCGS) method for the VCVAL, jointly training the video corpus retrieval and visual answer localization subtasks with the global-span matrix. We have reconstructed a dataset named MedVidCQA, on which the VCVAL task is benchmarked. Experimental results show that the proposed method outperforms other competitive methods both in the video corpus retrieval and visual answer localization subtasks. Most importantly, we perform detailed analyses on extensive experiments, paving a new path for understanding the instructional videos, which ushers in further research.
Exploring Interactions and Regulations in Collaborative Learning: An Interdisciplinary Multimodal Dataset
Authors: Yante Li, Yang Liu, KhÁnh Nguyen, Henglin Shi, Eija Vuorenmaa, Sanna Jarvela, Guoying Zhao
Date: 2022-10-11 00:00:00

Collaborative learning is an educational approach that enhances learning through shared goals and working together. Interaction and regulation are two essential factors related to the success of collaborative learning. Since the information from various modalities can reflect the quality of collaboration, a new multimodal dataset with cognitive and emotional triggers is introduced in this paper to explore how regulations affect interactions during the collaborative process. Specifically, a learning task with intentional interventions is designed and assigned to high school students aged 15 years old (N=81) in average. Multimodal signals, including video, Kinect, audio, and physiological data, are collected and exploited to study regulations in collaborative learning in terms of individual-participant-single-modality, individual-participant-multiple-modality, and multiple-participant-multiple-modality. Analysis of annotated emotions, body gestures, and their interactions indicates that our multimodal dataset with designed treatments could effectively examine moments of regulation in collaborative learning. In addition, preliminary experiments based on baseline models suggest that the dataset provides a challenging in-the-wild scenario, which could further contribute to the fields of education and affective computing.
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning
Authors: Linhai Zhuo, Yuqian Fu, Jingjing Chen, Yixin Cao, Yu-Gang Jiang
Date: 2022-10-11 00:00:00
Tasks: Cross-Domain Few-Shot, cross-domain few-shot learning, Few-Shot Learning, Meta-Learning, Transfer Learning

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio. To better transfer the knowledge, the proposed Mixup-3T network contains three branches with shared parameters for classifying classes in the source domain, target domain, and intermediate domain. To generate the optimal intermediate domain, the DRGN learns to generate an optimal mix ratio according to the performance on auxiliary target data. Then, the whole TGDM framework is trained via bi-level meta-learning so that TGDM can rectify itself to achieve optimal performance on target data. Extensive experimental results on several benchmark datasets verify the effectiveness of our method.
PP-StructureV2: A Stronger Document Analysis System
Authors: Chenxia Li, Ruoyu Guo, Jun Zhou, Mengtao An, Yuning Du, Lingfeng Zhu, Yi Liu, Xiaoguang Hu, dianhai yu
Date: 2022-10-11 00:00:00
Tasks: Key Information Extraction, Knowledge Distillation, Network Pruning, Relation Extraction, Table Recognition

A large amount of document data exists in unstructured form such as raw images without any text information. Designing a practical document image analysis system is a meaningful but challenging task. In previous work, we proposed an intelligent document analysis system PP-Structure. In order to further upgrade the function and performance of PP-Structure, we propose PP-StructureV2 in this work, which contains two subsystems: Layout Information Extraction and Key Information Extraction. Firstly, we integrate Image Direction Correction module and Layout Restoration module to enhance the functionality of the system. Secondly, 8 practical strategies are utilized in PP-StructureV2 for better performance. For Layout Analysis model, we introduce ultra light-weight detector PP-PicoDet and knowledge distillation algorithm FGD for model lightweighting, which increased the inference speed by 11 times with comparable mAP. For Table Recognition model, we utilize PP-LCNet, CSP-PAN and SLAHead to optimize the backbone module, feature fusion module and decoding module, respectively, which improved the table structure accuracy by 6\% with comparable inference speed. For Key Information Extraction model, we introduce VI-LayoutXLM which is a visual-feature independent LayoutXLM architecture, TB-YX sorting algorithm and U-DML knowledge distillation algorithm, which brought 2.8\% and 9.1\% improvement respectively on the Hmean of Semantic Entity Recognition and Relation Extraction tasks. All the above mentioned models and code are open-sourced in the GitHub repository PaddleOCR.
Stable and Efficient Adversarial Training through Local Linearization
Authors: Zhuorong Li, Daiwei Yu
Date: 2022-10-11 00:00:00

There has been a recent surge in single-step adversarial training as it shows robustness and efficiency. However, a phenomenon referred to as ``catastrophic overfitting" has been observed, which is prevalent in single-step defenses and may frustrate attempts to use FGSM adversarial training. To address this issue, we propose a novel method, Stable and Efficient Adversarial Training (SEAT), which mitigates catastrophic overfitting by harnessing on local properties that distinguish a robust model from that of a catastrophic overfitted model. The proposed SEAT has strong theoretical justifications, in that minimizing the SEAT loss can be shown to favour smooth empirical risk, thereby leading to robustness. Experimental results demonstrate that the proposed method successfully mitigates catastrophic overfitting, yielding superior performance amongst efficient defenses. Our single-step method can reach 51% robust accuracy for CIFAR-10 with $l_\infty$ perturbations of radius $8/255$ under a strong PGD-50 attack, matching the performance of a 10-step iterative adversarial training at merely 3% computational cost.
Race Bias Analysis of Bona Fide Errors in face anti-spoofing
Authors: Latifah Abduh, Ioannis Ivrissimtzis
Date: 2022-10-11 00:00:00
Tasks: Face Anti-Spoofing

The study of bias in Machine Learning is receiving a lot of attention in recent years, however, few only papers deal explicitly with the problem of race bias in face anti-spoofing. In this paper, we present a systematic study of race bias in face anti-spoofing with three key characteristics: the focus is on analysing potential bias in the bona fide errors, where significant ethical and legal issues lie; the analysis is not restricted to the final binary outcomes of the classifier, but also covers the classifier's scalar responses and its latent space; the threshold determining the operating point of the classifier is considered a variable. We demonstrate the proposed bias analysis process on a VQ-VAE based face anti-spoofing algorithm, trained on the Replay Attack and the Spoof in the Wild (SiW) databases, and analysed for bias on the SiW and Racial Faces in the Wild (RFW), databases. The results demonstrate that race bias is not necessarily the result of different mean response values among the various populations. Instead, it can be better understood as the combined effect of several possible characteristics of the response distributions: different means; different variances; bimodal behaviour; existence of outliers.
Printing variability of copy detection patterns
Authors: Roman Chaban, Olga Taran, Joakim Tutt, Yury Belousov, Brian Pulfer, Taras Holotyak, Slava Voloshynovskiy
Date: 2022-10-11 00:00:00
Tasks: Copy Detection

Copy detection pattern (CDP) is a novel solution for products' protection against counterfeiting, which gains its popularity in recent years. CDP attracts the anti-counterfeiting industry due to its numerous benefits in comparison to alternative protection techniques. Besides its attractiveness, there is an essential gap in the fundamental analysis of CDP authentication performance in large-scale industrial applications. It concerns variability of CDP parameters under different production conditions that include a type of printer, substrate, printing resolution, etc. Since digital off-set printing represents great flexibility in terms of product personalized in comparison with traditional off-set printing, it looks very interesting to address the above concerns for digital off-set printers that are used by several companies for the CDP protection of physical objects. In this paper, we thoroughly investigate certain factors impacting CDP. The experimental results obtained during our study reveal some previously unknown results and raise new and even more challenging questions. The results prove that it is a matter of great importance to choose carefully the substrate or printer for CDP production. This paper presents a new dataset produced by two industrial HP Indigo printers. The similarity between printed CDP and the digital templates, from which they have been produced, is chosen as a simple measure in our study. We found several particularities that might be of interest for large-scale industrial applications.
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
Authors: Yatai Ji, Junjie Wang, Yuan Gong, Lin Zhang, Yanru Zhu, Hongfa Wang, Jiaxing Zhang, Tetsuya Sakai, Yujiu Yang
Date: 2022-10-11 00:00:00
Proceeding: CVPR 2023 1
Tasks: Contrastive Learning, Image-text matching, Image-text Retrieval, Language Modeling, Language Modelling, Masked Language Modeling, Question Answering, Retrieval, Text Matching, Text Retrieval, Visual Entailment, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.
Gender Stereotyping Impact in Facial Expression Recognition
Authors: Iris Dominguez-Catena, Daniel Paternain, Mikel Galar
Date: 2022-10-11 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial Expression Recognition (FER) uses images of faces to identify the emotional state of users, allowing for a closer interaction between humans and autonomous systems. Unfortunately, as the images naturally integrate some demographic information, such as apparent age, gender, and race of the subject, these systems are prone to demographic bias issues. In recent years, machine learning-based models have become the most popular approach to FER. These models require training on large datasets of facial expression images, and their generalization capabilities are strongly related to the characteristics of the dataset. In publicly available FER datasets, apparent gender representation is usually mostly balanced, but their representation in the individual label is not, embedding social stereotypes into the datasets and generating a potential for harm. Although this type of bias has been overlooked so far, it is important to understand the impact it may have in the context of FER. To do so, we use a popular FER dataset, FER+, to generate derivative datasets with different amounts of stereotypical bias by altering the gender proportions of certain labels. We then proceed to measure the discrepancy between the performance of the models trained on these datasets for the apparent gender groups. We observe a discrepancy in the recognition of certain emotions between genders of up to $29 \%$ under the worst bias conditions. Our results also suggest a safety range for stereotypical bias in a dataset that does not appear to produce stereotypical bias in the resulting model. Our findings support the need for a thorough bias analysis of public datasets in problems like FER, where a global balance of demographic representation can still hide other types of bias that harm certain demographic groups.