Search Results

Semantic decoding, understood as predicting the semantic information carried by stimuli presented to subjects based on neural signals, is an active area of research. Previous studies have mainly focused on the visual perception process, with ...

Regularization is a common technique to prune neural networks which removes some weights with acceptable accuracy. Moreover, the alternating direction method of multipliers (ADMM) can quickly solve regularization problems. However, for learning ...

Autostereoscopic display technology, despite decades of development, has not achieved extensive application, primarily due to the daunting challenge of three-dimensional (3D) content creation for non-specialists. The emergence of Radiance Field as an ...

Reconstructing visual stimulus information from evoked brain activity is a significant task of visual decoding of the human brain. However, due to the limited size of published functional magnetic resonance imaging (fMRI) datasets, it is difficult ...

Few-shot video action recognition (FS-VAR) is a challenging task that requires models to have significant expressive power in order to identify previously unseen classes using only a few labeled examples. However, due to the limited number of ...

• We rectifies support sample by utilizing information from similar base prototypes.
• We remove the projection onto the shared centroid.
• Our method can be applied to various established FS-VAR frameworks.
• We emphasize the ...

In modern industrial environments, robots are expected to work close to human operators collaborating with them in completing various tasks, e.g., collaborative assembly or delivery of objects. However, it is difficult to take task optimization ...

• Hierarchical control method ensure safe human–robot Coexistence.
• Cast the Model Predictive Control as a Bayesian Inference problem
• Safety-critical controller with Stochastic Control Barrier Function to ensure safety

Continuous emotion recognition plays a crucial role in developing friendly and natural human-computer interaction applications. However, there exist two significant challenges unresolved in this field: how to effectively fuse complementary ...

Few-shot action recognition is a challenging problem aimed at learning a model capable of adapting to recognize new categories using only a few labeled videos. Recently, some works use attention mechanisms to focus on relevant regions to obtain ...

Document binarization is a crucial pre-processing step for various document analysis tasks. However, existing methods fail to accurately capture stroke edges, primarily due to the inherent limitations of vanilla convolutions and the absence of ...

• We propose a novel end-to-end document binarization method using gated convolutions.
• We solve the problem of imprecise stroke edge extraction by gated convolutions.
• We visualize the gating values to explain the effectiveness of ...

Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks. However, existing regression-based methods optimized for ...

As the self-attention mechanism offers powerful capabilities for capturing sequential relationships, it has become increasingly popular to use it for modeling user behavior sequences in recommender systems. However, the self-attention mechanism has a ...

Most existing ghost-imaging-based cryptographic key-distribution protocols involve one-way authentication, and the lack of a mutual authentication mechanism can easily lead to the tampering of key information. In this study, we ...

Human brain can recognize facial expressions in a short period of time. The complex multi-stage processing of neural activities in facial expression recognition is involved. Deep convolutional neural network (DCNN) has excellent performance ...

Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction model ...

Video-based group emotion recognition is an important research area in computer vision and is of great significance for the intelligent understanding of videos and human–computer interactions. Previous studies have adopted the traditional two-...

In recent years, action recognition has become a subject of focus in the field of computer vision. Interest has emerged regarding the recognition of previously unseen classes given a few labeled examples; this is known as few-shot video ...

Few Shot-Video Action Recognition (FS-VAR) has recently aroused great interest with the rise of meta-learning. The generalization ability of complex meta-learning is limited despite that it is effective and prevalent. In this work, we ...

Vehicular Ad Hoc Networks (VANETs) provide safety management and entertainment services in smart city scenarios. However, due to the highly time-varying network topology caused by vehicles’ movement and non-line-of-sight (NLOS) areas ...

Studies on visual encoding and reconstruction based on functional magnetic resonance imaging (fMRI) have inspired each other in recent years. However, as far as we know, there has not been any study that has achieved the reconstruction ...

Using a convolutional neural network to build visual encoding and decoding models of the human brain is a good starting point for the study on relationship between deep learning and human visual cognitive mechanism. However, related studies have not fully ...