pwc-archive/papers-with-abstracts huggingface dataset:
Quaternion Optimized Model with Sparse Regularization for Color Image Recovery
Authors: Liqiao Yang, Yang Liu, Kit Ian Kou
Date: 2022-04-19 00:00:00
Tasks: Matrix Completion

This paper addresses the color image completion problem in accordance with low-rank quatenrion matrix optimization that is characterized by sparse regularization in a transformed domain. This research was inspired by an appreciation of the fact that different signal types, including audio formats and images, possess structures that are inherently sparse in respect of their respective bases. Since color images can be processed as a whole in the quaternion domain, we depicted the sparsity of the color image in the quaternion discrete cosine transform (QDCT) domain. In addition, the representation of a low-rank structure that is intrinsic to the color image is a vital issue in the quaternion matrix completion problem. To achieve a more superior low-rank approximation, the quatenrion-based truncated nuclear norm (QTNN) is employed in the proposed model. Moreover, this model is facilitated by a competent alternating direction method of multipliers (ADMM) based on the algorithm. Extensive experimental results demonstrate that the proposed method can yield vastly superior completion performance in comparison with the state-of-the-art low-rank matrix/quaternion matrix approximation methods tested on color image recovery.
Topology and geometry of data manifold in deep learning
Authors: German Magai, Anton Ayzenberg
Date: 2022-04-19 00:00:00
Tasks: Deep Learning, Face Recognition, Open-Ended Question Answering, Topological Data Analysis

Despite significant advances in the field of deep learning in applications to various fields, explaining the inner processes of deep learning models remains an important and open question. The purpose of this article is to describe and substantiate the geometric and topological view of the learning process of neural networks. Our attention is focused on the internal representation of neural networks and on the dynamics of changes in the topology and geometry of the data manifold on different layers. We also propose a method for assessing the generalizing ability of neural networks based on topological descriptors. In this paper, we use the concepts of topological data analysis and intrinsic dimension, and we present a wide range of experiments on different datasets and different configurations of convolutional neural network architectures. In addition, we consider the issue of the geometry of adversarial attacks in the classification task and spoofing attacks on face recognition systems. Our work is a contribution to the development of an important area of explainable and interpretable AI through the example of computer vision.
CorrGAN: Input Transformation Technique Against Natural Corruptions
Authors: Mirazul Haque, Christof J. Budnik, Wei Yang
Date: 2022-04-19 00:00:00
Tasks: Generative Adversarial Network

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.
Self-Supervised Equivariant Learning for Oriented Keypoint Detection
Authors: Jongmin Lee, Byungjin Kim, Minsu Cho
Date: 2022-04-19 00:00:00
Proceeding: CVPR 2022 1
Tasks: Camera Pose Estimation, Keypoint Detection, Pose Estimation, Self-Supervised Learning, Translation

Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Existing learning-based methods for keypoint detection rely on standard translation-equivariant CNNs but often fail to detect reliable keypoints against geometric variations. To learn to detect robust oriented keypoints, we introduce a self-supervised learning framework using rotation-equivariant CNNs. We propose a dense orientation alignment loss by an image pair generated by synthetic transformations for training a histogram-based orientation map. Our method outperforms the previous methods on an image matching benchmark and a camera pose estimation benchmark.
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors
Authors: Nyee Thoang Lim, Meng Yi Kuan, Muxin Pu, Mei Kuan Lim, Chun Yong Chong
Date: 2022-04-19 00:00:00
Tasks: Adversarial Attack, DeepFake Detection, Face Swapping

Deepfakes utilise Artificial Intelligence (AI) techniques to create synthetic media where the likeness of one person is replaced with another. There are growing concerns that deepfakes can be maliciously used to create misleading and harmful digital contents. As deepfakes become more common, there is a dire need for deepfake detection technology to help spot deepfake media. Present deepfake detection models are able to achieve outstanding accuracy (>90%). However, most of them are limited to within-dataset scenario, where the same dataset is used for training and testing. Most models do not generalise well enough in cross-dataset scenario, where models are tested on unseen datasets from another source. Furthermore, state-of-the-art deepfake detection models rely on neural network-based classification models that are known to be vulnerable to adversarial attacks. Motivated by the need for a robust deepfake detection model, this study adapts metamorphic testing (MT) principles to help identify potential factors that could influence the robustness of the examined model, while overcoming the test oracle problem in this domain. Metamorphic testing is specifically chosen as the testing technique as it fits our demand to address learning-based system testing with probabilistic outcomes from largely black-box components, based on potentially large input domains. We performed our evaluations on MesoInception-4 and TwoStreamNet models, which are the state-of-the-art deepfake detection models. This study identified makeup application as an adversarial attack that could fool deepfake detectors. Our experimental results demonstrate that both the MesoInception-4 and TwoStreamNet models degrade in their performance by up to 30\% when the input data is perturbed with makeup.
Image Data Augmentation for Deep Learning: A Survey
Authors: Suorong Yang, Weikang Xiao, Mengchen Zhang, Suhan Guo, Jian Zhao, Furao Shen
Date: 2022-04-19 00:00:00
Tasks: Data Augmentation, Deep Learning, Diversity, image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation, Survey

Deep learning has achieved remarkable results in many computer vision tasks. Deep neural networks typically rely on large amounts of training data to avoid overfitting. However, labeled data for real-world applications may be limited. By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data. As an effective way to improve the sufficiency and diversity of training data, data augmentation has become a necessary part of successful application of deep learning models on image data. In this paper, we systematically review different image data augmentation methods. We propose a taxonomy of reviewed methods and present the strengths and limitations of these methods. We also conduct extensive experiments with various data augmentation methods on three typical computer vision tasks, including semantic segmentation, image classification and object detection. Finally, we discuss current challenges faced by data augmentation and future research directions to put forward some useful research guidance.
A Tour of Visualization Techniques for Computer Vision Datasets
Authors: Bilal Alsallakh, Pamela Bhattacharya, Vanessa Feng, Narine Kokhlikyan, Orion Reblitz-Richardson, Rahul Rajan, David Yan
Date: 2022-04-19 00:00:00
Tasks: Data Visualization, Survey

We survey a number of data visualization techniques for analyzing Computer Vision (CV) datasets. These techniques help us understand properties and latent patterns in such data, by applying dataset-level analysis. We present various examples of how such analysis helps predict the potential impact of the dataset properties on CV models and informs appropriate mitigation of their shortcomings. Finally, we explore avenues for further visualization techniques of different modalities of CV datasets as well as ones that are tailored to support specific CV tasks and analysis needs.
A Region-Based Deep Learning Approach to Automated Retail Checkout
Authors: Maged Shoman, Armstrong Aboah, Alex Morehead, Ye Duan, Abdulateef Daud, Yaw Adu-Gyamfi
Date: 2022-04-18 00:00:00
Tasks: Deep Learning, object-detection, Object Detection

Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking. Towards this end, reliable deep learning models that enable automated product counting for fast customer checkout can make this goal a reality. In this work, we propose a novel, region-based deep learning approach to automate product counting using a customized YOLOv5 object detection pipeline and the DeepSORT algorithm. Our results on challenging, real-world test videos demonstrate that our method can generalize its predictions to a sufficient level of accuracy and with a fast enough runtime to warrant deployment to real-world commercial settings. Our proposed method won 4th place in the 2022 AI City Challenge, Track 4, with an F1 score of 0.4400 on experimental validation data.
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance
Authors: Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, Edward Raff
Date: 2022-04-18 00:00:00
Tasks: Image Generation

Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models. We demonstrate a novel methodology for both tasks which is capable of producing images of high visual quality from text prompts of significant semantic complexity without any training by using a multimodal encoder to guide image generations. We demonstrate on a variety of tasks how using CLIP [37] to guide VQGAN [11] produces higher visual quality outputs than prior, less flexible approaches like DALL-E [38], GLIDE [33] and Open-Edit [24], despite not being trained for the tasks presented. Our code is available in a public repository.
Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond
Authors: Kang Liao, Xiangyu Xu, Chunyu Lin, Wenqi Ren, Yunchao Wei, Yao Zhao
Date: 2022-04-18 00:00:00
Tasks: Depth Estimation, Image Outpainting, Image Super-Resolution, object-detection, Object Detection, Super-Resolution

Image outpainting gains increasing attention since it can generate the complete scene from a partial view, providing a valuable solution to construct {360\textdegree} panoramic images. As image outpainting suffers from the intrinsic issue of unidirectional completion flow, previous methods convert the original problem into inpainting, which allows a bidirectional flow. However, we find that inpainting has its own limitations and is inferior to outpainting in certain situations. The question of how they may be combined for the best of both has as yet remained under-explored. In this paper, we provide a deep analysis of the differences between inpainting and outpainting, which essentially depends on how the source pixels contribute to the unknown regions under different spatial arrangements. Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a seamless cylinder. Nevertheless, straightforwardly applying the cylinder-style convolution often generates visually unpleasing results as it discards important positional information. To address this issue, we further present a learnable positional embedding strategy to incorporate the missing component of positional encoding into the cylinder convolution, which significantly improves the panoramic results. It is noted that while developed for image outpainting, the proposed algorithm can be effectively extended to other panoramic vision tasks, such as object detection, depth estimation, and image super-resolution. Code will be made available at \url{https://github.com/KangLiao929/Cylin-Painting}.
Spot the Difference: A Novel Task for Embodied Agents in Changing Environments
Authors: Federico Landi, Roberto Bigazzi, Marcella Cornia, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara
Date: 2022-04-18 00:00:00

Embodied AI is a recent research area that aims at creating intelligent agents that can move and operate inside an environment. Existing approaches in this field demand the agents to act in completely new and unexplored scenes. However, this setting is far from realistic use cases that instead require executing multiple tasks in the same environment. Even if the environment changes over time, the agent could still count on its global knowledge about the scene while trying to adapt its internal representation to the current state of the environment. To make a step towards this setting, we propose Spot the Difference: a novel task for Embodied AI where the agent has access to an outdated map of the environment and needs to recover the correct layout in a fixed time budget. To this end, we collect a new dataset of occupancy maps starting from existing datasets of 3D spaces and generating a number of possible layouts for a single environment. This dataset can be employed in the popular Habitat simulator and is fully compliant with existing methods that employ reconstructed occupancy maps during navigation. Furthermore, we propose an exploration policy that can take advantage of previous knowledge of the environment and identify changes in the scene faster and more effectively than existing agents. Experimental results show that the proposed architecture outperforms existing state-of-the-art models for exploration on this new setting.
DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning
Authors: Chengcheng Guo, Bo Zhao, Yanbing Bai
Date: 2022-04-18 00:00:00
Tasks: Active Learning, Continual Learning, Deep Learning, Neural Architecture Search

Coreset selection, which aims to select a subset of the most informative training samples, is a long-standing learning problem that can benefit many downstream tasks such as data-efficient learning, continual learning, neural architecture search, active learning, etc. However, many existing coreset selection methods are not designed for deep learning, which may have high complexity and poor generalization performance. In addition, the recently proposed methods are evaluated on models, datasets, and settings of different complexities. To advance the research of coreset selection in deep learning, we contribute a comprehensive code library, namely DeepCore, and provide an empirical study on popular coreset selection methods on CIFAR10 and ImageNet datasets. Extensive experiments on CIFAR10 and ImageNet datasets verify that, although various methods have advantages in certain experiment settings, random selection is still a strong baseline.
Active Learning Helps Pretrained Models Learn the Intended Task
Authors: Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah Goodman
Date: 2022-04-18 00:00:00
Tasks: Active Learning

Models can fail in unpredictable ways during deployment due to task ambiguity, when multiple behaviors are consistent with the provided training data. An example is an object classifier trained on red squares and blue circles: when encountering blue squares, the intended behavior is undefined. We investigate whether pretrained models are better active learners, capable of disambiguating between the possible tasks a user may be trying to specify. Intriguingly, we find that better active learning is an emergent property of the pretraining process: pretrained models require up to 5 times fewer labels when using uncertainty-based active learning, while non-pretrained models see no or even negative benefit. We find these gains come from an ability to select examples with attributes that disambiguate the intended behavior, such as rare product categories or atypical backgrounds. These attributes are far more linearly separable in pretrained model's representation spaces vs non-pretrained models, suggesting a possible mechanism for this behavior.
Inductive Biases for Object-Centric Representations in the Presence of Complex Textures
Authors: Samuele Papa, Ole Winther, Andrea Dittadi
Date: 2022-04-18 00:00:00
Tasks: Object, Segmentation, Style Transfer

Understanding which inductive biases could be helpful for the unsupervised learning of object-centric representations of natural scenes is challenging. In this paper, we systematically investigate the performance of two models on datasets where neural style transfer was used to obtain objects with complex textures while still retaining ground-truth annotations. We find that by using a single module to reconstruct both the shape and visual appearance of each object, the model learns more useful representations and achieves better object separation. In addition, we observe that adjusting the latent space size is insufficient to improve segmentation performance. Finally, the downstream usefulness of the representations is significantly more strongly correlated with segmentation quality than with reconstruction accuracy.
Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer
Authors: Yangrun Hu, Yuanfan Guo, Fan Zhang, Mingda Wang, Tiancheng Lin, Rong Wu, Yi Xu
Date: 2022-04-18 00:00:00
Tasks: Cancer Classification, Classification, Transfer Learning

Much progress has been made in the deep neural network (DNN) based diagnosis of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is less investigated because of the limited data. Based on the insight that mass data is sufficient and shares the same knowledge structure with non-mass data of identifying the malignancy of a lesion based on the ultrasound image, we propose a novel transfer learning framework to enhance the generalizability of the DNN model for non-mass BUS with the help of mass BUS. Specifically, we train a shared DNN with combined non-mass and mass data. With the prior of different marginal distributions in input and output space, we employ two domain alignment strategies in the proposed transfer learning framework with the insight of capturing domain-specific distribution to address the issue of domain shift. Moreover, we propose a cross-domain semantic-preserve data generation module called CrossMix to recover the missing distribution between non-mass and mass data that is not presented in training data. Experimental results on an in-house dataset demonstrate that the DNN model trained with combined data by our framework achieves a 10% improvement in AUC on the malignancy prediction task of non-mass BUS compared to training directly on non-mass data.
Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis
Authors: Yuanfan Guo, Canqian Yang, Tiancheng Lin, Chunxiao Li, Rui Zhang, Yi Xu
Date: 2022-04-18 00:00:00
Tasks: Contrastive Learning

Previous deep learning based Computer Aided Diagnosis (CAD) system treats multiple views of the same lesion as independent images. Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic relationship between different views of a lesion, which is inconsistent with the traditional diagnosis where sonographers analyze a lesion from at least two views. In this paper, we propose a multi-task framework that complements Benign/Malignant classification task with lesion recognition (LR) which helps leveraging relationship among multiple views of a single lesion to learn a complete representation of the lesion. To be specific, LR task employs contrastive learning to encourage representation that pulls multiple views of the same lesion and repels those of different lesions. The task therefore facilitates a representation that is not only invariant to the view change of the lesion, but also capturing fine-grained features to distinguish between different lesions. Experiments show that the proposed multi-task framework boosts the performance of Benign/Malignant classification as two sub-tasks complement each other and enhance the learned representation of ultrasound images.
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport
Authors: Yingtao Tian, Marco Cuturi, David Ha
Date: 2022-04-18 00:00:00
Tasks: Diversity, Image Generation

Recent advances in deep learning, such as powerful generative models and joint text-image embeddings, have provided the computational creativity community with new tools, opening new perspectives for artistic pursuits. Text-to-image synthesis approaches that operate by generating images from text cues provide a case in point. These images are generated with a latent vector that is progressively refined to agree with text cues. To do so, patches are sampled within the generated image, and compared with the text prompts in the common text-image embedding space; The latent vector is then updated, using gradient descent, to reduce the mean (average) distance between these patches and text cues. While this approach provides artists with ample freedom to customize the overall appearance of images, through their choice in generative models, the reliance on a simple criterion (mean of distances) often causes mode collapse: The entire image is drawn to the average of all text cues, thereby losing their diversity. To address this issue, we propose using matching techniques found in the optimal transport (OT) literature, resulting in images that are able to reflect faithfully a wide diversity of prompts. We provide numerous illustrations showing that OT avoids some of the pitfalls arising from estimating vectors with mean distances, and demonstrate the capacity of our proposed method to perform better in experiments, qualitatively and quantitatively.
Hand Geometry Based Recognition with a MLP Classifier
Authors: Marcos Faundez-Zanuy, Miguel A. Ferrer-Ballester, Carlos M. Travieso-González, Virginia Espinosa-Duro
Date: 2022-04-16 00:00:00

This paper presents a biometric recognition system based on hand geometry. We describe a database specially collected for research purposes, which consists of 50 people and 10 different acquisitions of the right hand. This database can be freely downloaded. In addition, we describe a feature extraction procedure and we obtain experimental results using different classification strategies based on Multi Layer Perceptrons (MLP). We have evaluated identification rates and Detection Cost Function (DCF) values for verification applications. Experimental results reveal up to 100% identification and 0% DCF