Reviewerly/exHarmony huggingface dataset:
Semantic Guidance Learning for High-Resolution Non-homogeneous Dehazing

High-resolution non-homogeneous dehazing aims to generate a clear image from a 4000 × 6000 image with non-homogeneous haze. To the best of our knowledge, this task is a new challenge that was not addressed in the previous literature. To address this issue, we propose semantic-guided loss functions for high-resolution non-homogeneous dehazing. We find semantic information contains strong texture and color prior. Thus, we proposed to adopt the pre-trained model to generate the semantic mask to guide the neural network during the training phase. On the other hand, to handle the non-homogeneous dehazing process in the high-resolution scenario, we adjust the kernel size of the model to increase the receptive field. Furthermore, to deal with the different image sizes during the training and the testing phase, several post-processing methods are applied to improve the high-resolution non-homogeneous dehazing. Several experiments performed on challenging benchmark show that the proposed model achieves competitive performance in the NTIRE 2023 HR NonHomogeneous Dehazing Challenge.
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose ‘Few Shot anOMaly dEtection’ (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on ‘few’ examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, ‘Stop Loss’, improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available; https://github.com/niamhbelton/FewSOME.
Shared Interest…Sometimes: Understanding the Alignment between Human Perception, Vision Architectures, and Saliency Map Techniques

Empirical studies have shown that attention-based architectures outperform traditional convolutional neural networks (CNN) in terms of accuracy and robustness. As a result, attention-based architectures are increasingly used in high-stakes domains such as radiology and wildlife conservation to aid in decision-making. However, understanding how attention-based architectures compare to CNNs regarding alignment with human perception is still under-explored. Previous studies exploring how vision architectures align with human perception evaluate a single architecture with multiple explainability techniques or multiple architectures with a single explainability technique. Through an empirical analysis, we investigate how two attention-based architectures and two CNNs for two saliency map techniques align with the ground truth for human perception on 100 images from an interpretability benchmark dataset. Using the Shared Interest metrics, we found that CNNs align more with human perception when using the XRAI saliency map technique. However, we found the opposite for Grad-CAM. We discuss the implications of our analysis for human-centered explainable AI and introduce directions for future work.
Shape-Net: Room Layout Estimation from Panoramic Images Robust to Occlusion using Knowledge Distillation with 3D Shapes as Additional Inputs

Estimating the layout of a room from a single-shot panoramic image is important in virtual/augmented reality and furniture layout simulation. This involves identifying three-dimensional (3D) geometry, such as the location of corners and boundaries, and performing 3D reconstruction. However, occlusion is a common issue that can negatively impact room layout estimation, and this has not been thoroughly studied to date. It is possible to obtain 3D shape information of rooms as drawings of buildings and coordinates of corners from image datasets, thus we propose providing both 2D panoramic and 3D information to a model to effectively deal with occlusion. However, simply feeding 3D information to a model is not sufficient to utilize the shape information for an occluded area. Therefore, we improve the model by introducing 3D Intersection over Union (IoU) loss to effectively use 3D information. In some cases, drawings are not available or the construction deviates from a drawing. Considering such practical cases, we propose a method for distilling knowledge from a model trained with both images and 3D information to a model that takes only images as input. The proposed model, which is called Shape-Net, achieves state-of-the-art (SOTA) performance on benchmark datasets. We also confirmed its effectiveness in dealing with occlusion through significantly improved accuracy on images with occlusion compared with existing models.
KBody: Towards general, robust, and aligned monocular whole-body estimation

KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body’s parameters. Acknowledging the importance of high quality correspondences, it leverages "virtual joints" to improve fitting performance, disentangles the optimization between the pose and shape parameters, and integrates asymmetric distance fields to strike a balance in terms of pose and shape capturing capacity, as well as pixel alignment. We also show that generative model inversion offers a strong appearance prior that can be used to complete partial human images and used as a building block for generalized and robust monocular body fitting. Project page: https://klothed.github.io/KBody.
Content-Adaptive Downsampling in Convolutional Neural Networks

Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network’s receptive field and decrease computational cost. However, this comes at the price of losing granularity in the feature maps, limiting the ability to correctly understand images or recover fine detail in dense prediction tasks. To address this, common practice is to replace the last few downsampling operations in a CNN with dilated convolutions, allowing to retain the feature map resolution without reducing the receptive field, albeit increasing the computational cost. This allows to trade off predictive performance against cost, depending on the output feature resolution. By either regularly downsampling or not downsampling the entire feature map, existing work implicitly treats all regions of the input image and subsequent feature maps as equally important, which generally does not hold. We propose an adaptive downsampling scheme that generalizes the above idea by allowing to process informative regions at a higher resolution than less informative ones. In a variety of experiments, we demonstrate the versatility of our adaptive downsampling strategy and empirically show that it improves the cost-accuracy trade-off of various established CNNs.
Fast Local Thickness

We propose a fast algorithm for the computation of local thickness in 2D and 3D. Compared to the conventional algorithm, our fast algorithm yields local thickness in just a fraction of the time. In our algorithm, we first compute the distance field of the object and then iteratively dilate the selected parts of the distance field. In every iteration, we employ small structuring elements, which makes our approach fast. Our algorithm is implemented in Python and is freely available as a pip-installable module. Besides giving a detailed description of our method, we test our implementation on 2D images and 3D volumes. In 2D, we compute the ground truth using the conventional local thickness methods, where the distance field is dilated with increasingly larger circular structuring elements. We use this as a reference to evaluate the quality of our results. In 3D, we have no ground truth since it would be too time-consuming to compute. Instead, we compare our results with the golden standard method provided by BoneJ. In both 2D and 3D, we compare with another Python-based approach from PoreSpy. Our algorithm performs equally well or better than other approaches, but significantly faster.
Robustness with Query-efficient Adversarial Attack using Reinforcement Learning

A measure of robustness against naturally occurring distortions is key to the safety, success, and trustworthiness of machine learning models on deployment. We propose an adversarial black-box attack that adds minimum Gaussian noise distortions to input images to make machine learning models misclassify. We used a Reinforcement Learning (RL) agent as a smart hacker to explore the input images to add minimum distortions to the most sensitive regions to induce misclassification. The agent employs a smart policy also to remove noises introduced earlier, which has less impact on the trained model at a given state. This novel approach is equivalent to doing a deep tree search to add noises without an exhaustive search, leading to faster and optimal convergence. Also, this adversarial attack method effectively measures the robustness of image classification models with the misclassification inducing minimum L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> distortion of Gaussian noise similar to many naturally occurring distortions. Furthermore, the proposed black-box L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> adversarial attack tool beats state-of-the-art competitors in terms of the average number of queries by a significant margin with a 100% success rate while maintaining a very competitive L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> score, despite limiting distortions to Gaussian noise. For the ImageNet dataset, the average number of queries achieved by the proposed method for ResNet-50, Inception-V3, and VGG-16 models are 42%, 32%, and 31% better than the state-of-the-art "Square-Attack" approach while maintaining a competitive L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> .Demo: https://tinyurl.com/yr8f7x9t
ReidTrack: Reid-only Multi-target Multi-camera Tracking

Multi-target multi-camera tracking of persons in indoor scenarios such as retail stores or warehouses enables efficient placement of products and improvement of working processes. In this work, we propose the ReidTrack framework, which performs the task solely based on peoples’ visual appearances. In theory, accurate person re-identification is able to solve the whole task without the need for additional and complex scene models or post-processing steps. ReidTrack is based on clustering appearance embeddings with a mechanism to avoid identity switches caused by detection bounding boxes showing the body parts of multiple individuals. With only a robust person re-identification model and the real-time detector YOLOv8 and without any auxiliary information, such as complex scene models, our approach ranks fourth concerning Track 1 of the 2023 AI City Challenge.
The Impact of Smart Technologies on SME Sustainability: The Mediation Effect of Sustainability Strategy – Literature Review

The purpose of this research is to investigate the influence of smart technologies on small and medium-sized enterprise (SME) sustainability, and to examine the mediation effect of the SME sustainability strategy in the relationship between smart technologies and the sustainable performance of SMEs in the Kingdom of Bahrain. According to scholars, the sustainability concept includes "environmental sustainability," "social sustainability," and "profitability" factors. The research concludes that smart technology has a positive effect on profitability while stabilizing the environment and social wellbeing performance. In addition, the SME sustainability strategy is critical for utilizing smart manufacturing in 4.0 technologies such as cloud computing and artificial intelligence. Also, the research found that the institution and division of innovation theories contribute as the lens of the study. Literature review gaps summarized that in Bahrain, there is a lack of research and statistical reports related to utilizing technology in SMEs. Although the scholars of governmental support in terms of supporting SME growth was limited to depending on and on conducting full information in order to gain a deep insight into the strategic moves of SMEs.
On Text Localization in End-to-End OCR-Free Document Understanding Transformer Without Text Localization Supervision

This paper presents a simple yet effective approach for weakly supervised text localization in end-to-end visual document understanding (VDU) models. The traditional approach in VDU is to utilize off-the-shelf OCR engines in conjunction with natural language understanding models. However, to simplify the VDU pipeline and improve efficiency, recent research has focused on OCR-free document understanding transformers. These models have a limitation in that they do not provide the location of the text to the user. To alleviate it, we propose a simple yet effective method for text localization in OCR-free models that does not require any additional supervision, such as bounding box annotations. The method is based on properties of attention mechanism, and is able to output text areas with competitive high accuracy compared to other supervised methods. The proposed method can be easily applied to most existing OCR-free models, making it an attractive solution for practitioners in the field. We validate the method through experiments on document parsing benchmarks, and the results demonstrate its effectiveness in generalizing to various camera-captured document images, such as, receipts and business cards. The implementation will be available at https://github.com/clovaai/donut .
The Importance of Context for Sentiment Analysis in Dialogues

Sentiment Analysis (SA) can be applied to dialogues to determine the emotional tone throughout the conversation.This is beneficial for dialogue systems because it may improve human-computer interaction.For instance, in case of negative sentiment, the system may switch to a human operator who can handle the situation more effectively.However, given that dialogues are a series of utterances, the context, including the previous text, plays a crucial role in analyzing the current sentiment.Our aim is to investigate the importance of context when monitoring the sentiment of every utterance during a conversation.To accomplish this goal, we assess sentiment analysis in dialogues with varying levels of context, specifically differing in the number and author of preceding utterances.We conduct experiments on Portuguese customer-support conversations, with each utterance manually labeled as having negative or non-negative sentiment.We test a wide range of text classification approaches, from traditional, as simplicity should not be overlooked, to more recent methods, as they are more likely to achieve better performances.Results indicate that the relevance of context varies.However, context assumes particular value in human-computer dialogues, when considering both speakers, and in shorter human-human conversations, when focusing on the client.Moreover, the best classifier for both scenarios, based on BERT, achieves the highest scores when considering the context.
Specular surface deformation measurement based on projected-speckle deflectometry with digital image correlation

Traditional deflectometry techniques suffer from the issue of out-of-focus image acquisition since the camera is pointed directly at the specular surface, and the image quality is greatly disturbed by ambient light. In this study, the projected-speckle deflectometry based on the digital image correlation (DIC) technique is proposed. The projection speckle pattern is reflected by specular surface onto the screen, at which the camera is oriented directly. The deformation of specular sample is represented by the speckle pattern variation, and the camera collects changed images to further calculate slope and deformation. Quantitative deformation experiments are employed to validate the measurement accuracy of the system, and the average error of the five measured values is within 4.55%. Wafer measurement experiments are carried out to compare deformed results with those of the laser interferometer and optical 3D metrology, and the effects of projector vibration, electronic noise and out-of-plane movements are analyzed in detail. The root mean square (RMS) of measured deformation resolution can be as low as 0.11μm. With high measurement efficiency, this method avoids the problem of out-of-focus image acquisition due to excessive surface deformation and reduces the interference of ambient light.
Anomaly Detection in Scientific Datasets using Sparse Representation

As the size and complexity of high-performance computing (HPC) systems keep growing, scientists' ability to trust the data produced is paramount due to potential data corruption for various reasons, which may stay undetected. While employing machine learning-based anomaly detection techniques could relieve scientists of such concern, it is practically infeasible due to the need for labels for volumes of scientific datasets and the unwanted extra overhead associated. In this paper, we exploit spatial sparsity profiles exhibited in scientific datasets and propose an approach to detect anomalies effectively. Our method first extracts block-level sparse representations of original datasets in the transformed domain. Then it learns from the extracted sparse representations and builds the boundary threshold between normal and abnormal without relying on labeled data. Experiments using real-world scientific datasets show that the proposed approach requires 13% on average (less than 10% in most cases and as low as 0.3%) of the entire dataset to achieve competitive detection accuracy (70.74%-100.0%) as compared to two state-of-the-art unsupervised techniques.
Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness

Evolutionary Computation algorithms have been used to solve optimization problems in relation with architectural, hyper-parameter or training configuration, forging the field known today as Neural Architecture Search. These algorithms have been combined with other techniques such as the pruning of Neural Networks, which reduces the complexity of the network, and the Transfer Learning, which lets the import of knowledge from another problem related to the one at hand. The usage of several criteria to evaluate the quality of the evolutionary proposals is also a common case, in which the performance and complexity of the network are the most used criteria. This work proposes MO-EvoPruneDeepTL, a multi-objective evolutionary pruning algorithm. MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models. We carry out different experiments with several datasets to assess the benefits of our proposal. Results show that our proposal achieves promising results in all the objectives, and direct relation are presented among them. The experiments also show that the most influential neurons help us explain which parts of the input images are the most relevant for the prediction of the pruned neural network. Lastly, by virtue of the diversity within the Pareto front of pruning patterns produced by the proposal, it is shown that an ensemble of differently pruned models improves the overall performance and robustness of the trained networks.
Erase and Repair: An Efficient Box-Free Removal Attack on High-Capacity Deep Hiding

Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In this paper, we challenge the robustness of existing deep hiding schemes by preventing the recovery of secret images, building on our in-depth study of state-of-the-art deep hiding schemes and their vulnerabilities. Leveraging our analysis, we first propose a simple box-free removal attack on deep hiding that does not require any prior knowledge of the deep hiding schemes. To improve the removal performance on the deep hiding schemes that may be enhanced by adversarial training, we further design a more powerful removal attack, efficient box-free removal attack (EBRA), which employs image inpainting techniques to remove secret images from container images. In addition, to ensure the effectiveness of our attack and preserve the fidelity of the processed container images, we design an erasing phase based on the locality of deep hiding to remove secret information and then make full use of the visual information of container images to repair the erased visual content. Extensive evaluations show our method can completely remove secret images from container images with negligible impact on the quality of container images.
Reversible Gates: A Paradigm Shift in Computing

The reversible gate has been one of the emerging research areas that ensure a continual process of innovation trends that explore and utilizes the resources. This review paper provides a comprehensive overview of reversible gates, including their fundamental principles, design methodologies, and various applications. It also analyzes the reversible gates, comparing them based on metrics such as Quantum Cost, Complexity, and other performance evaluation measures. The analysis of several reversible gates is presented in this paper and provides a comprehensive overview of reversible gates, encompassing their fundamental principles, design methodologies, and diverse applications. Reversible logic circuits allow for the production of both unique outputs and distinct input combinations. The majority of the findings about the reversible gates from previous research papers are discussed and contrasted. All the reversible gates that have been proposed till now are presented in tabular form and the parameters are discussed to help the researchers to find every detail related to the reversible gates. To highlight our understanding, we have ended most of the sections with questions. The inclusion of questions is likely intended to stimulate further discussion and promote a deeper understanding of the material presented in this paper. These questions can serve as prompts for readers to reflect on the content and potentially explore related research directions or areas of improvement.