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 …

The brain's connectivity is locally dense and globally sparse, forming a small-world graph—
a principle prevalent in the evolution of various species, suggesting a universal solution for …

Recent studies have integrated convolution into transformers to introduce inductive bias and
improve generalization performance. However, the static nature of conventional convolution …

This paper identifies significant redundancy in the query-key interactions within self-attention
mechanisms of diffusion transformer models, particularly during the early stages of …

Segment anything model (SAM) has been widely applied to various downstream tasks for its
excellent performance and generalization capability. However, SAM exhibits three …

The Transformer architecture has been widely applied in the field of image segmentation
due to its powerful ability to capture long-range dependencies. However, its ability to capture …

Transformers have shown superior performance on various vision tasks. Their large
receptive field endows Transformer models with higher representation power than their CNN …

Self-attention is the key mechanism of transformers, which are the essential building blocks
of modern foundation models. Recent studies have shown that pure self-attention suffers …

Abstract Vision Transformer (ViT) faces obstacles in wide application due to its huge
computational cost. Almost all existing studies on compressing ViT adopt the manner of …

In this work, we seek to learn multiple mainstream vision tasks concurrently using a unified
network, which is storage-efficient as numerous networks with task-shared parameters can …