The 3-D object detection with light detection and ranging (LiDAR) point clouds is a
challenging problem, which requires 3-D scene understanding, yet this task is critical to …

Recent advancements in deep convolutional neural networks have significantly improved
the performance of saliency prediction. However, the manual configuration of the neural …

Conventional RGB‐T salient object detection treats RGB and thermal modalities equally to
locate the common salient regions. However, the authors observed that the rich colour and …

Live streaming commerce thrives with a rich tapestry of multimodal information that
intertwines with various entities, including the anchor, the commodities, and the live …

Learning-based methods have become popular in co-salient object detection (CoSOD).
However, existing methods suffer from two challenging issues, including mining the inter …

Recently, many salient object detection (SOD) methods decouple image features into body
features and edge features, which imply a new development direction in the field of SOD …

RGB-thermal salient object detection (RGB-T SOD) has rapidly developed and achieved
excellent detection results. Unfortunately, the significant impact of illumination on salient …

Saliency Detection is a crucial undertaking in the realm of vision computing, with a goal to
identify the visual prominent regions within an input image. The method of automated …

Aiming at the problems that crowded objects have serious occlusion and few effective
features in complex scenes, an improved YOLOv7 algorithm is proposed to solve the …

Multimodal sentiment analysis is an emerging field of research that aims to extract and
integrate semantic information from multimodal data to recognize moods and emotions of …