This study introduces a novel methodology for evaluating tennis player performance through the application of Hierarchical Markov Models (HMM). By dissecting the game's flow and modeling the transitions between states, our proposed model captures the ...

With the surge in realistic text tampering, detecting fraudulent text in images has gained prominence for maintaining information security. However, the high costs associated with professional text manipulation and annotation limit the ...

• A new dataset for text manipulation detection with diverse handcraft manipulations
• An asymmetric dual-stream baseline framework to exploit different transformed domains
• An aggregation hub and a fusion module for efficient multi-...

Digital watermarking technology has wide application prospects due to its excellent performance in copyright protection and traceability. However, there are still some gaps in robustness when it comes to arbitrary scaling and cropping attacks ...

• A robust watermarking framework for arbitrary scaling and cropping attacks is proposed, which improves the attack degree of geometric attacks.
• An algorithm based on machine learning to estimate arbitrary scaling attack parameters is ...

Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that classifies images in the target domain when the source domain has labeled samples, and the target domain has unlabeled samples. Due to the complexity of imaging ...

Unsupervised domain adaptive vehicle density estimation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, which has received extensive attention due to its practicality and effectiveness. ...

In recent years, Vision-Language Pre-training (VLP) models have demonstrated rich prior knowledge for multimodal alignment, prompting investigations into their application in Specific Domain Image-Text Retrieval(SDITR) such as Text-Image Person Re-...

Recent advances in vision-language pre-trained models like CLIP have greatly enhanced general domain image-text retrieval performance. This success has led scholars to develop methods for applying CLIP to Specific Domain Image-Text Retrieval (SDITR) ...

Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poisson coding are more robust than Artificial Neural Networks (ANNs) on ...

Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio. However, existing countermeasures still serve a classification ...

Currently, the renewable energy power generation industry has entered a new stage, and accurate renewable energy power generation prediction is of great significance for the strategic planning of energy systems. However, renewable energy power ...

The design and development of text-based knowledge graph completion (KGC) methods leveraging textual entity descriptions are at the forefront of research. These methods involve advanced optimization techniques such as soft prompts and contrastive ...

The use of Virtual Reality (VR) technology, especially in medical rehabilitation, has expanded to include tactile cues along with visual stimuli. For patients with upper limb hemiplegia, tangible handles with haptic stimuli could improve their ability to ...

Accurate and timely diagnosis of COVID-like virus is of paramount importance for lifesaving. In this work, deep learning techniques are applied to lung CT image segmentation for accurate disease diagnosis. We discuss the limitations of current ...

In this paper, we explore the integration of parameterized quantum pulses with the contextual subspace method. The advent of parameterized quantum pulses marks a transition from traditional quantum gates to a more flexible and efficient approach to ...

With the development of Metaverse technology, the avatar in Metaverse has faced serious security and privacy concerns. Analyzing facial features to distinguish between genuine and manipulated facial videos holds significant research importance for ...

Pathology image classification is one of the most essential auxiliary processes in cancer diagnosis. To overcome the problem of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple ...

• Prototype-driven pseudo-bag division scheme is proposed for WSI classification.
• Attention-based prototype generation is designed for pseudo-bags.
• A pseudo-bag division by instance similarities is designed.

Cross-modal image-text retrieval (CMITR) has been a high-value research topic for more than a decade. In most of the previous studies, the data for all tasks are trained as a single set. However, in reality, a more likely scenario is that the ...

• A novel continual learning for cross-modal image-text retrieval (CLCMR) method has been proposed.
• A multilayer domain-selective attention (MDSA) module has been used to select parameters specific to particular previous tasks.
• A ...

It is better to use physiotherapy to solve sub-health problems than to medicine. Identifying acupoints is an important part of the physiotherapy. Currently, identifying acupoints is highly dependent on doctors, which is time-consuming and labor-...

Transactions are used to guarantee data consistency and integrity in Database Management Systems (DBMSs), and have become an indispensable component in DBMSs. However, faulty designs and implementations of DBMSs' transaction processing mechanisms can ...

Improving the generalization ability in offline reinforcement learning (RL) has received much attention in recent years. Existing adversarial RL approaches use adversarial training for the policy improvement, thus enhancing the generalization ability of ...