A study from EPFL reveals why humans excel at recognizing objects from fragments while AI struggles, highlighting the ...

When you look at the world around you, it might feel like your eyes and brain work in perfect sync—taking in a smooth, ...

In this paper, a graph convolutional network (GCN)-based multi-object tracking (MOT) algorithm, consisting of a module for extracting the initial features and a module for updating the features, that ...

Next, graph convolutional networks were employed to extract node embeddings from the network, which were further integrated via a scaled attention fusion module, generating high-quality node ...

The experimental findings in this section show how well the Multi-View United Transformer Block of the Graph Attention Network detects ASD. Using a variety of neuroimaging modalities, including as MRI ...

Query-based 3D Multi-Object Tracking (MOT) facilitates seamless integration into end-to-end frameworks. Many existing methods adopt the tracking-by-attention paradigm, utilizing track queries for ...

Then, a spatial multi-scale graph convolution network based on the attention mechanism is constructed to obtain the spatial features from joint nodes, while a temporal graph convolution network in the ...

However, target association is still immature and less effective in complex scenarios. In the proposed tracking system, several candidates surrounding each detected pedestrian are selected sparsely, ...

Researchers have adapted deep learning techniques in a multi-object tracking framework, overcoming short-term occlusion and achieving remarkable performance without sacrificing computational speed.