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Self-supervised pre-training of image encoders is omnipresent in the literature, particularly following the introduction of Masked autoencoders (MAE). Current efforts attempt to learn object-centric representations from motion in videos. In ...

The traditional Siamese network based object tracking algorithms suffer from high computational complexity, making them difficult to run on embedded devices. Moreover, when faced with long-term tracking tasks, their success rates significantly ...

In industrial production systems, detecting malfunctions or unexpected behavior in devices early is crucial to avoid critical situations for both production plants and workers. In this context, we propose an unsupervised anomaly detection model ...

Machine learning models often struggle to generalize accurately when tested on new class distributions that were not present in their training data. This is a significant challenge for real-world applications that require quick adaptation without ...

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 ...

Recent advancements in the field of visual tracking have been propelled by the amalgamation of Siamese networks and region proposal networks, which have demonstrated excellent competitive accuracy while remaining computationally efficient. ...

• A visual tracking approach with extreme point graph-guided annotation is proposed.
• An extreme-pooling operation is introduced.
• A U-shaped backbone network is designed.
• A multi-task tracking loss function is applied.
• ...

The efficiency of deep neural networks is increasing, and so is the amount of annotated data required for training them. We propose a solution improving the learning process of a classification network with less labeled data. Our approach is to ...

• We propose a solution to learn a classification network with less labeled data.
• The principle is to provide the classifier a binary mask as a simple shape prior.
• We resort to a Siamese architecture and feed it with images and shape ...

An artificially generated dorsal hand vein database called 'GenVeins' (see Beukes, 2023) is developed in this study for the purpose of acquiring sets of fictitious training and validation individuals which are large enough to represent the entire ...

Compared to other well-known biometric technologies based on physiological traits (e.g., fingerprint, iris, and face), heart biometrics are more robust to presentation attacks and are particularly suitable for continuous/periodic recognition. ...

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• The first study on the interoperability between ECG and PPG biometrics, based on Siamese CNNs.
• Novel CNNs for processing two-dimensional representations of PPG signals.
• Ad-hoc algorithm for training Siamese CNNs, suitable for small-...

Surface defect detection is an important task in the field of manufacturing, and dealing with imbalanced data is a challenge that has been addressed using methods such as anomaly detection and data augmentation. However, optical devices pose a ...

• A Generalized Well Neural Network via Template-Testing comparison is proposed.
• Dual-Attention and Recurrent Residual mechanisms proposed for noise elimination.
• Our approach generalizes to new batches via template collection in FMS.

For Siamese-based trackers in the field of single object tracking, cross-correlation operation plays an important role. However, the cross-correlation essentially uses target feature to locally linearly match the search region, which leads to ...

• An attention-based tracking framework using multi-scale strategyis presented.
• The parallel multi-scale template encodercan better generalize template features.
• The decoder that use modulation makesattention operation suitable for ...

Content-based histopathology image retrieval can be a useful technique for help in diagnosing various diseases. The process of retrieving images is often time-consuming and challenging due to the need for high-dimensional features when trying to ...

Graph representation learning is the encoding of graph nodes into a low-dimensional representation space, which can effectively improve graph information representation while reducing the information dimensionality. To overcome the heavy reliance ...

Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of artificial intelligence (AI) because of its versatility and effectiveness. As an emerging force ...

To address the multi-scale problem and interference of differences between data in remote sensing image segmentation, a multi-scale Siamese dual decoding network is proposed. The twin network is used as the backbone network for parallel processing and ...

Since diffusion-weighted imaging (DWI) images are high-dimensional medical images with rich texture features, existing traditional zero-watermarking algorithms cannot effectively protect the copyright of DWI images while satisfying the conditions ...

Recently, Siamese trackers have achieved remarkable tracking performance. However, challenges such as accurate feature representation of targets with different spatial regions and the utilization of diverse target temporal states still ...

Community-based Question and Answering (CQA) platforms have a huge number of users, resulting in numerous duplicate questions with similar intent from different users. Effectively detecting duplicate questions can improve the findability of ...

Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item ...

Recommender systems are widely adopted as an increasing research and development area, since they provide users with diverse and useful information tailored to their needs. Several strategies have been proposed, and in most of them some concept of ...