Object detection is a crucial task in computer vision (CV). With the rapid advancement of
Transformer-based models in natural language processing (NLP) and various visual tasks …

Transformers have demonstrated impressive expressiveness and transfer capability in
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …

Multi-scale features are of great importance in encoding objects with scale variance in object
detection tasks. A common strategy for multi-scale feature extraction is adopting the classic …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

The visual feature pyramid has shown its superiority in both effectiveness and efficiency in a
variety of applications. However, current methods overly focus on inter-layer feature …

Convolutional neural network (CNN) can extract effective semantic features, so it was widely
used for remote sensing image change detection (CD) in the latest years. CNN has acquired …

Convolution and self-attention are two powerful techniques for representation learning, and
they are usually considered as two peer approaches that are distinct from each other. In this …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing
methods track by associating detections through motion-based and appearance-based …

Modern change detection (CD) has achieved remarkable success by the powerful
discriminative ability of deep convolutions. However, high-resolution remote sensing CD …