Exploring refined dual visual features cross-combination for image captioning.

For current image caption tasks used to encode region features and grid features Transformer-based encoders have become commonplace, because of their multi-head self-attention mechanism, the encoder can better capture the relationship between different regions in the image and contextual information. However, stacking Transformer blocks necessitates quadratic computation through self-attention to visual features, not only resulting in the computation of numerous redundant features but also significantly increasing computational overhead. This paper presents a novel Distilled Cross-Combination Transformer (DCCT) network.

SiamBAN: Target-Aware Tracking With Siamese Box Adaptive Network.

Variation of scales or aspect ratios has been one of the main challenges for tracking. To overcome this challenge, most existing methods adopt either multi-scale search or anchor-based schemes, which use a predefined search space in a handcrafted way and therefore limit their performance in complicated scenes. To address this problem, recent anchor-free based trackers have been proposed without using prior scale or anchor information.

Improving Alzheimer's Disease Classification by Combining Multiple Measures.

Several anatomical magnetic resonance imaging (MRI) markers for Alzheimer's disease (AD) have been identified. Cortical gray matter volume, cortical thickness, and subcortical volume have been used successfully to assist the diagnosis of Alzheimer's disease including its early warning and developing stages, e.g.