Pattern Recognition Letters

Alzheimer’s Disease impairs the memory and cognitive function of patients, and early intervention can effectively mitigate its deterioration. Most existing methods for Alzheimer’s analysis rely solely on medical images, ignoring the impact of ...

• We propose a multi-task module to predict AD and corresponding clinical indicators.
• We designed an attention-based method to utilize the information between tasks.
• Our model can provide brain regions more relevant to tasks.
• Our ...

Artificial Intelligence has found wide application, but also poses risks due to unintentional or malicious tampering during deployment. Regular checks are therefore necessary to detect and prevent such risks. Fragile watermarking is a technique ...

• Fragile watermarking for rigorous neural network integrity detection.
• Watermarking ensures 100% tampering detection, precise location, and recovery.
• Watermarking fragility by combining Mutual- and Self-neural net parameters.
• ...

• Design and train 55-layer CNN model on the CIFAR-100 dataset.
• Build two-stream network to extract spatial and temporal features from the images.
• Feature optimization is performed using a Genetic algorithm.

Human Gait Recognition (HGR) is referred to as a biometric tactic that is broadly used for the recognition of an individual by using the pattern of walking. There are some key factors such as angle variation, clothing variation, foot shadows, and ...

Social media has evolved into a widely influential information source in contemporary society. However, the widespread use of social media also enables the rapid spread of fake news, which can pose a significant threat to national and social ...

• We propose a novel fake news detection model that combines the graph convolution network and BERT.
• We employ the co-attention mechanism to obtain better results and achieve training convergence in a shorter time.
• We conduct ...

Sentiment classification and emotion recognition are two close related tasks in NLP. However, most of the recent studies have treated them as two separate tasks, where the shared knowledge are neglected. In this paper, we propose a multi-task ...

• Sentiment classification and emotion recognition are two correlative tasks in NLP.
• But they are often treated as separative tasks.
• In contrast to differences, commonalities of sentiment and emotion are often ignored.
• Keys in ...

The classical JPEG compression is a rich source of cues for forensic image analysis. However, this compression standard will in the near future be complemented by a new, highly efficient learning-based compression standard called JPEG AI. JPEG AI ...

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• Characterization of forensic cues of different AI-based image codecs.
• Frequency domain and autocorrelation exhibit periodic artifacts.
• Artifacts stem likely from upsampling operations and homogeneous regions.
• Experiments show ...

In this paper, we propose a new lightweight Channel-Spatial Knowledge Distillation (CSKD) method to handle the task of efficient image semantic segmentation. More precisely, we investigate the KD approach that train a compressed neural network ...

• Heavy semantic segmentation methods require high computational costs.
• The knowledge distillation is adopted for efficient semantic Segmentation.
• The spatial and channel distillations through self-attention between teacher and ...

Extracting knowledge from data streams requires fast incremental algorithms that are able to handle unlimited processing and ever-changing data with finite memory. A strategy for this challenge is the use of ensembles owing to their ability to ...

• Classification algorithm for data streams fast and with low memory consumption.
• Ensemble that incorporates only instances incorrectly classified in the training step.
• Ensemble with random feature subspace size.
• Classifier ...

In natural language processing, the general approach to sentiment analysis involves a pre-training and fine-tuning paradigm using pre-trained language models combined with classifier models. Recently, numerous studies have applied prompts not ...

• A soft prompt-based classification method, SPACE, is proposed for sentiment analysis.
• SPACE utilizes a modified attention pattern to consider only the attentions between prompt and the input text.
• A MLM task for prompts is ...

We present a framework for completing high-fidelity 3D facial UV maps from single-face image. Despite the success of Generative Adversarial Networks (GANs) in this area, generating accurate UV maps from in-the-wild images remains challenging. Our ...

• A novel scheme for generating high-fidelity face UV maps from single portrait.
• Map and Edit network allows precise facial pose control with StyleGAN methods.
• We address the domain gap in 3DMM StyleGAN edits due to photometric loss.

Despite the wide variety of methods developed for synthetic image attribution, most of them can only attribute images generated by models or architectures included in the training set and do not work with unknown architectures, hindering their ...

• New verification framework for open-set architecture attribution of synthetic images.
• Tested with several types of generative architectures in closed and open set scenarios.
• Generalization tests prove that the system can verify ...

The popularity of social networks as primary mediums for sharing visual content has made it crucial for forensic experts to identify the original platform of multimedia content. Various methods address this challenge, but the constant emergence ...

• We investigate continual learning methods for social network identification.
• We exploit a state-of-the-art dual-branch neural network designed for this task.
• We define two realistic experimental scenarios on multiple datasets.
• ...

• Explore multifractal properties of dairy cow behavior using MFDFA and deep wavelet transform.
• Identify behavioral patterns correlating with cows' physiological states.
• Develop a model using fractal indices for predicting cattle ...

The study conduct an in-depth exploration of the multifractal characteristics of dairy cows behavioral data, aiming to reveal their complexity and representation in behavioral patterns. By means of Multifractal Detrended Fluctuation Analysis (...

Matrix factorization (MF) is a simple collaborative filtering technique that achieves superior recommendation accuracy by decomposing the user–item interaction matrix into user and item latent matrices. Because the model typically learns each ...

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• Recommender model to capture user/item hierarchies simultaneously in factorization.
• Hierarchical embeddings are optimized with a single gradient descent method.
• Obtained clusters capable of summarizing of interactions and providing ...

Optical flow estimation is an important hot research in computer vision. Although existing methods had got a considerable progress in improving their performance, they still have drawbacks, such as heavily computational burden, inaccurate pixel-...

• A novel pyramidal deep Taylor expansion (PDTE) framework for optical flow estimation.
• Each derivative part calculated by a global motion aggregation (GMA) method.
• Both quantitative and qualitative results verify the effectiveness ...

In this paper, we propose a frame-part-activated deep reinforcement learning (FPA-DRL) for action prediction. Most existing methods for action prediction utilize the evolution of whole frames to model actions, which cannot avoid the noise of the ...

• We design the part-activated module to enhance the action-related parts of features.
• We design the frame-activated module to reduce the redundancy of frames.
• We achieved very competitive results of state-of-the-arts on three ...

Face recognition models often encounter various unseen domains and environments in real-world applications, leading to unsatisfactory performance due to the open-set nature of face recognition. Models trained on central datasets may exhibit poor ...

• Proposed generalized face recognition aimed to handle unknown target domains without model updates or fine-tuning.
• Proposed contrastive learning-based approach to address problem of illumination and motion blur for registered ...

Predicting cardiovascular disease has been a challenging task, as assessing samples based on a single view of information may be insufficient. Therefore, in this paper, we focus on the challenge of predicting cardiovascular disease using multi-...

• Our method considers multi-view cardiac data to provide comprehensive and accurate information for diagnosis.
• Our method overcomes the limitations of sample-efficient and label-efficient data.
• Our method captures global ...