Reviewerly/exHarmony huggingface dataset:
Liver Segmentation via Learning Cross-Modality Content-Aware Representation

Liver segmentation has important clinical implications using computed tomography (CT) and magnetic resonance imaging (MRI). MRI has complementary characteristics to improve the accuracy of medical analysis tasks. Compared with MRI, CT images of the liver are more abundant and readily available. Ideally, it is promising to transfer learned knowledge from the CT images with labels to the target domain MR images by unsupervised domain adaptation. In this paper, we propose a novel framework, i.e. cross-modality content-aware representation (CMCAR), to alleviate domain shifts for cross-modality semantic segmentation. The proposed framework mainly consists of two modules in an end-to-end manner. One module is an image-to-image translation network based on the generative adversarial method and representation disentanglement. The other module is a mutual learning model to reduce further the semantic gap between synthesis images and real images. Our model is validated on two cross-modality semantic segmentation datasets. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods.
CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock

In recent years, watermarking technology has been widely used as a common information hiding technique in the fields of copyright protection, authentication, and data privacy protection in digital media. However, the development of watermark attack techniques has lagged behind. Improving the efficiency of watermark attack techniques and effectively attacking watermarks has become an urgent problem to be solved. Therefore, this paper proposes a watermark attack network called CAWNet. Firstly, this paper designs a convolution-based watermark attack module (CWABlock), which introduces channel attention mechanism. By replacing fully connected layers with global average pooling layers, the parameter quantity of the network is reduced and the computational efficiency is improved, enabling effective attacks on watermark information. Secondly, in the training phase, we utilize a large-scale real-world image dataset for training and employ data augmentation strategies to enhance the robustness of the network. Finally, we conduct ablation experiments on CWABlock, attention mechanism, and other modules, as well as comparative experiments on different watermark attack methods. The experimental results demonstrate significant improvements in the effectiveness of the proposed watermark attack approach.
Salient Object Detection Using Reciprocal Learning

Typically, Objects with the same semantics may not always stand out in images with diverse backgrounds. Therefore, accurate salient object detection depends on considering both the foreground and background. Motivated by this observation, we proposed a novel reciprocal mechanism that considers the mutual relationships between background and foreground for salient object detection. First, we design a patulous U-shape framework comprising a shared encoder branch and two parallel decoder branches for extracting the foreground and background responses, respectively. Second, we propose a novel reciprocal feature interaction (RFI) module for the two decoder branches, allowing them to learn necessary information from each other adaptively. The RFI module primarily consists of a reciprocal transformer (RT) block that utilizes modulated window-based multi-head cross-attention (MW-MCA) to capture mutual dependencies between elements of the foreground and background features within the current two windows. Through the RFI module, the two decoder branches can mutually benefit each other and generate more discriminative foreground and background features. Additionally, we introduce a cooperative loss (CL) to guide the learning of foreground and background branches, which encourages our network to obtain more accurate predictions with clear boundaries and less uncertain areas. Finally, a simple but effective fusion strategy is utilized to produce the final saliency map. Extensive experiments on five benchmark datasets demonstrate the significant superiority of our method over the state-of-the-art approaches.
GLViG: Global and Local Vision GNN May Be What You Need for Vision

In this article, we propose a novel vision architecture termed GLViG, which leverages graph neural networks (GNNs) to capture local and important global information in images. To achieve this, GLViG represents image patches as graph nodes and constructs two types of graphs to encode the information, which are subsequently processed by GNNs to enable efficient information exchange between image patches, resulting in superior performance. In order to address the quadratic computational complexity challenges posed by high-resolution images, GLViG adaptively samples the image patches and optimizes computational complexity to linear. Finally, to enhance the adaptation of GNNs to the 2D image structure, we use Depth-wise Convolution dynamically generated positional encoding as a solution to the fixed-size and static limitations of absolute position encoding in ViG. The extensive experiments on image classification, object detection, and image segmentation demonstrate the superiority of the proposed GLViG architecture. Specifically, the GLViG-B1 architecture achieves a significant improvement on ImageNet-1K when compared to the state-of-the-art GNN-based backbone ViG-Tiny (80.7% vs. 78.2%). Additionally, our proposed GLViG model surpasses popular computer vision models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Vision MLPs. We believe that our method has great potential to advance the capabilities of computer vision and bring a new perspective to the design of new vision architectures.
Unsupervised Image-to-Image Translation with Style Consistency

Unsupervised Image-to-Image Translation (UNIT) has gained significant attention due to its strong ability of data augmentation. UNIT aims to generate a visually pleasing image by synthesizing an image’s content with another’s style. However, current methods cannot ensure that the style of the generated image matches that of the input style image well. To overcome this issue, we present a new two-stage framework, called Unsupervised Image-to-Image Translation with Style Consistency (SC-UNIT), for improving the style consistency between the image of the style domain and the generated image. The key idea of SC-UNIT is to build a style consistency module to prevent the deviation of the learned style from the input one. Specifically, in the first stage, SC-UNIT trains a content encoder to extract the multiple-layer content features wherein the last-layer’s feature can represent the abstract domain-shared content. In the second stage, we train a generator to integrate the content features with the style feature to generate a new image. During the generation process, dynamic skip connections and multiple-layer content features are used to build multiple-level content correspondences. Furthermore, we design a style reconstruction loss to make the style of the generated image consistent with that of the input style image. Numerous experimental results show that our SC-UNIT outperforms state-of-the-art methods in image quality, style diversity, and style consistency, even for domains with significant visual differences. The code is available at https://github.com/GZHU-DVL/SC-UNIT .
Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis

Class incremental learning (CIL) aims to recognize both the old and new classes along the increment tasks. Deep neural networks in CIL suffer from catastrophic forgetting and some approaches rely on saving exemplars from previous tasks, known as the exemplar-based setting, to alleviate this problem. On the contrary, this paper focuses on the Exemplar-Free setting with no old class sample preserved. Balancing the plasticity and stability in deep feature learning with only supervision from new classes is more challenging. Most existing Exemplar-Free CIL methods report the overall performance only and lack further analysis. In this work, different methods are examined with complementary metrics in greater detail. Moreover, we propose a simple CIL method, Rotation Augmented Distillation (RAD), which achieves one of the top-tier performances under the Exemplar-Free setting. Detailed analysis shows our RAD benefits from the superior balance between plasticity and stability. Finally, more challenging exemplar-free settings with fewer initial classes are undertaken for further demonstrations and comparisons among the state-of-the-art methods.
The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review

As digital transformation continues to advance, organisations are becoming increasingly aware of the benefits that modern technologies offer. However, with greater technology adoption comes a higher risk of cyber security threats and attacks. Therefore, there is a need for more advanced measures to protect against constantly evolving threats. One potential solution is the use of Artificial Intelligence (AI). The aim of this research paper was to conduct a systematic literature review (SLR) to assess the impact of AI-based technologies on organisational cyber security and determine their effectiveness compared to traditional cyber security approaches. The PRISMA flow diagram was used to guide the review process. Peer-reviewed articles from 2018 to 2023 were included from EBSCO Host, Google Scholar, Science Direct, ProQuest & SCOPUS and 73 remaining articles were synthesised. The results revealed that AI can impact cybersecurity throughout it's entire life cycle, yielding benefits like automation, threat intelligence, and improved cyber defense. Nevertheless, it also brings challenges like adversarial attacks and the need for high-quality data, which could lead to the inefficiency of AI. These results affirm the positive influence of AI on cybersecurity, enhancing effectiveness and resilience. These findings provide a solid foundation for further research in the field of organisational cybersecurity. These results can help organisations make informed decisions on AI implementations by offering an impartial view of its impacts.
Decoupled Contrastive Learning for Long-Tailed Distribution

Self-supervised contrastive learning is popularly used to obtain powerful representation models. However, unlabeled data in the real world naturally exhibits a long-tailed distribution, making the traditional instance-wise contrastive learning unfair to tail samples. Recently, some improvements have been made from the perspective of model, loss, and data to make tail samples highly evaluated during training, but most of them explicitly or implicitly assume that the sample with a large loss is the tail. We argue that due to the lack of hard negatives, tail samples usually occupy a small loss at the initial stage of training, which will make them eliminated at the beginning of training. To address this issue, we propose a simple but effective two-stage learning scheme that decouples traditional contrastive learning to discover and enhance tail samples. Specifically, we identify the sample with a small loss in Stage I while a large loss in Stage II as the tail. With the discovered tail samples, we generate hard negatives for them based on their neighbors, which will balance the distribution of the hard negatives in training and help learn better representation. Additionally, we design the weight inversely proportional or proportional to the loss in each stage to achieve fairer training by reweighting. Extensive experiments on multiple unlabeled long-tailed datasets demonstrate the superiority of our DCL compared with the state-of-the-art methods. The code will be released soon.
Adaptive Cluster Assignment for Unsupervised Semantic Segmentation

Unsupervised semantic segmentation (USS) aims to identify semantically consistent regions and assign correct categories without annotations. Since the self-supervised pre-trained vision transformer (ViT) can provide pixel-level features containing rich class-aware information and object distinctions, it has recently been widely used as the backbone for unsupervised semantic segmentation. Although these methods achieve exceptional performance, they often rely on the parametric classifiers and therefore need the prior about the number of categories in advance. In this work, we investigate the process of clustering adaptively for the current mini-batch of images without having prior on the number of categories and propose Adaptive Cluster Assignment Module (ACAM) to replace parametric classifiers. Furthermore, we optimize ACAM to generate weights via the introduction of contrastive learning, which is used to re-weight features, thereby generating semantically consistent clusters. Additionally, we leverage image-text pre-trained models, CLIP, to assign specific labels to each mask obtained from clustering and pixel assignment. Our method achieves new state-of-the-art results in COCO-Stuff and Cityscapes datasets.
AGST-LSTM: The ConvLSTM Model Combines Attention and Gate Structure for Spatiotemporal Sequence Prediction Learning

Spatiotemporal sequence prediction learning generates one or more frames of images by learning from multiple frames of historical input. Most current spatiotemporal sequence prediction learning methods do not adequately consider the importance of long-term features for spatial reconstruction. Based on the convolutional LSTM (ConvLSTM) network unit, this paper adds a memory storage unit that updates information through the original memory unit in the ConvLSTM unit and uses the same zigzag memory flow as the PredRNN network, which can focus on long-term and short-term spatiotemporal features at the same time. Then, an attention module is proposed to extract important information from the long-term hidden state and aggregate it with the short-term hidden state, expand the temporal feeling field of the hidden state, and propose the attention gate spatiotemporal LSTM (AGST-LSTM) model, which further enhances the model’s capacity to catch the spatiotemporal correlation. This paper validates the model through two different prediction tasks. The AGST-LSTM model has competitive performance compared to the comparative model to some degree, as exhibited in experiments.
Few-Shot Infrared Image Classification with Partial Concept Feature

Few infrared image samples will bring a catastrophic blow to the recognition performance of the model. Existing few-shot learning methods most utilize the global features of object to classify infrared image. However, their inability to sufficiently extract the most representative feature for classification results in a degradation of recognition performance. To tackle the aforementioned shortcomings, we propose a few-shot infrared image classification method based on the partial conceptual features of the object. It enables the flexible selection of local features from targets. With the integration of these partial features into the concept feature space, the method utilizes Euclidean distance for similarity measurement to accomplish infrared target classification. The experimental results demonstrate that our proposed method outperforms previous approaches on a new infrared few-shot recognition dataset. It effectively mitigates the adverse effects caused by background blurring in infrared images and significantly improving classification accuracy.
Two-Step Projection of Sparse Discrimination Between Classes for Unsupervised Domain Adaptation

In the past few years, researchers have developed domain adaptive (DA) techniques which aim to address the domain shift between training and testing sets. However, most existing unsupervised domain adaptation techniques only use a projection matrix to train the classifier, which do not impose any restrictions on the same class samples. In this paper, we introduce a novel approach to unsupervised domain adaptation, called two-step projection of sparse discrimination between classes for unsupervised domain adaptation (TSPSDC). Unlike existing methods that use a single matrix for classifier learning, TSPSDC leverages two-step projection learning and integrates inter-class sparsity constraints to extract domain-invariant features from the class level. Specifically, 1) aiming to mitigate any adverse consequences arising from domain shift, distribution alignments are implemented to decrease the distribution disparity between the source and target domains in the shared subspace. 2) Our approach involves integrating two types of regularization: manifold regularization and inter-class sparsity regularization. The feature representation exhibits the same row sparsity structure within each class. Simultaneously, the distance between similar classes is minimized as a result. The resulting feature representation is thus advantageous for achieving highly discriminative representations. Extensive experimental results on three different sets of data demonstrate that the proposed approach outperforms many contemporary techniques for domain adaptation.
Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-Based Data

Graph structure data has seen widespread utilization in modeling and learning representations, with dynamic graph neural networks being a popular choice. However, existing approaches to dynamic representation learning suffer from either discrete learning, leading to the loss of temporal information, or continuous learning, which entails significant computational burdens. Regarding these issues, we propose an innovative dynamic graph neural network called Event Sparse Net (ESN). By encoding time information adaptively as snapshots and there is an identical amount of temporal structure in each snapshot, our approach achieves continuous and precise time encoding while avoiding potential information loss in snapshot-based methods. Additionally, we introduce a lightweight module, namely Global Temporal Attention, for computing node representations based on temporal dynamics and structural neighborhoods. By simplifying the fully-connected attention fusion, our approach significantly reduces computational costs compared to the currently best-performing methods. We assess our methodology on four continuous/discrete graph datasets for link prediction to assess its effectiveness. In comparison experiments with top-notch baseline models, ESN achieves competitive performance with faster inference speed.
End-to-End Unsupervised Style and Resolution Transfer Adaptation Segmentation Model for Remote Sensing Images

For remote sensing image unsupervised domain adaptation, there are differences in resolution except for feature differences between source and target domains. An end-to-end unsupervised domain adaptation segmentation model for remote sensing images is proposed to reduce the image style and resolution differences between the source and target domains. First, a generative adversarial-based style transfer network with residual connection, scale consistency module, and perceptual loss with class balance weights is proposed. It reduces the image style and resolution differences between the two domains and maintains the original structural information while transferring. Second, the visual attention network (VAN) that considers both spatial and channel attention is used as the feature extraction backbone network to improve the feature extraction capability. Finally, the style transfer and segmentation tasks are unified in an end-to-end network. Experimental results show that the proposed model effectively alleviates the performance degradation caused by different features and resolutions. The segmentation performance is significantly improved compared to advanced domain adaptation segmentation methods.
Frequency and Spatial Domain Filter Network for Visual Object Tracking

Cross-correlation serves as the core similarity calculation operation in Siamese-based trackers, and generally produces response maps with high values at the target center. During this process, global context, including boundary and surrounding background of the target, which is conducive to target localization and bounding box regression, has been overlooked. In this work, we propose a Frequency and Spatial domain Filter Network (FSFNet) for visual object tracking, which exploits abundant global context in the frequency domain and enhances target representation in the spatial domain. First, frequency filters generated from template and search patches are applied to the target, capturing and enhancing valuable frequency components. These enhanced frequency components describe the global regions of interest in the spatial domain. Second, spatial domain convolutions are adopted to highlight local details of the target. Compared with mechanisms including depth-wise correlation, pixel-wise correlation, and transformer, our method provides more accurate tracking results. Experiments on five benchmarks show that our tracker obtains competitive results. For example, our tracker achieves an AUC score of 81.2% on TrackingNet, outperforming the state-of-the-art two-stream tracker TrDiMP by 2.8% while running at 50 FPS.
Enhancing Model Robustness Against Adversarial Attacks with an Anti-adversarial Module

Due to the rapid development of artificial intelligence technologies, such as deep neural networks in recent years, the subsequent emergence of adversarial samples poses a great threat to the security of deep neural network models. In order to defend threats brought by adversarial attacks, the recent mainstream method is to use adversarial training methods to add adversarial samples into model’s training process. Although such a type of method can defend against adversarial attacks, it requires increased computing resources and time, and reduces the accuracy of the original samples. Adversarial defense method that conduct anti-adversity in the inference stage. Inspired by adversarial example generation methods, we propose a defense against adversarial in the inference phase of the model. The adversarial sample generation is to add disturbances in the direction of maximizing the loss function after obtaining the sample gradient. Therefore, we add a perturbation in the opposite direction of the adversarial example generated before the sample is fed into the network. The main advantages of our method are that the method requires less computing resources and time; and our method can effectively improve the robust accuracy of the model against adversarial attacks. As a summary, the research content in this paper can stabilize adversarial training, alleviate the high resource consumption of adversarial training, and improve the overall robust performance of the model, which is of great significance to adversarial defense.
Typical Yet Unlikely and Normally Abnormal: The Intuition Behind High-Dimensional Statistics

Abstract Normality, in the colloquial sense, has historically been considered an aspirational trait, synonymous with ideality. The arithmetic average and, by extension, statistics including linear regression coefficients, have often been used to characterize normality, and are often used as a way to summarize samples and identify outliers. We provide intuition behind the behavior of such statistics in high dimensions, and demonstrate that even for datasets with a relatively low number of dimensions, data start to exhibit a number of peculiarities which become severe as the number of dimensions increases. Whilst our main goal is to familiarize researchers with these peculiarities, we also show that normality can be better characterized with ‘typicality’, an information theoretic concept relating to entropy. An application of typicality to both synthetic and real-world data concerning political values reveals that in multi-dimensional space, to be ‘normal’ is actually to be atypical. We briefly explore the ramifications for outlier detection, demonstrating how typicality, in contrast with the popular Mahalanobis distance, represents a viable method for outlier detection.