We introduce a novel method using a new generative model that automatically learns
effective representations of the target and background appearance to detect, segment and …

Optical flow is an indispensable building block for various important computer vision tasks,
including motion estimation, object tracking, and disparity measurement. In this work, we …

We present CLUSTSEG, a general, transformer-based framework that tackles different
image segmentation tasks (ie, superpixel, semantic, instance, and panoptic) through a …

This paper presents ClusterFormer, a universal vision model that is based on the Clustering
paradigm with TransFormer. It comprises two novel designs: 1) recurrent cross-attention …

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 combination of infrared and visible videos aims to gather more comprehensive feature
information from multiple sources and reach superior results on various practical tasks, such …

The task of point cloud segmentation, comprising semantic, instance, and panoptic
segmentation, has been mainly tackled by designing task-specific network architectures …

As the size of transformer-based models continues to grow, fine-tuning these large-scale
pretrained vision models for new tasks has become increasingly parameter-intensive …

Location determination finds wide applications in daily life. Instead of existing efforts devoted
to localizing tourist photos captured by perspective cameras, in this article, we focus on …

Fine-tuning large vision-language models is a challenging task. Prompt tuning approaches
have been introduced to learn fixed textual or visual prompts while freezing the pre-trained …