As a special type of transformer, vision transformers (ViTs) can be used for various computer
vision (CV) applications. Convolutional neural networks (CNNs) have several potential …

Transformer plays a vital role in the realms of natural language processing (NLP) and
computer vision (CV), specially for constructing large language models (LLM) and large …

The quadratic computation complexity of self-attention has been a persistent challenge
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

Transformers have recently shown superior performances on various vision tasks. The large,
sometimes even global, receptive field endows Transformer models with higher …

Modern detection transformers (DETRs) use a set of object queries to predict a list of
bounding boxes, sort them by their classification confidence scores, and select the top …

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

Abstract Generalized Referring Expression Segmentation (GRES) extends the scope of
classic RES to refer to multiple objects in one expression or identify the empty targets absent …

Vision Transformers convert images to sequences by slicing them into patches. The size of
these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher …

Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs
more efficient by removing redundant information in the processed tokens. While different …