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 …

Abstract The CLIP and Segment Anything Model (SAM) are remarkable vision foundation
models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is …

Video segmentation aims to segment and track every pixel in diverse scenarios accurately.
In this paper, we present Tube-Link, a versatile framework that addresses multiple core tasks …

Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

We introduce a new task--language-driven video inpainting which uses natural language
instructions to guide the inpainting process. This approach overcomes the limitations of …

We present MosaicFusion, a simple yet effective diffusion-based data augmentation
approach for large vocabulary instance segmentation. Our method is training-free and does …

In this work, we focus on open vocabulary instance segmentation to expand a segmentation
model to classify and segment instance-level novel categories. Previous approaches have …

Current video anomaly detection (VAD) approaches with weak supervisions are inherently
limited to a closed-set setting and may struggle in open-world applications where there can …

Deep neural networks (DNNs) have proven explicit contributions in making autonomous
driving cars and related tasks such as semantic segmentation, motion tracking, object …

This paper considers zero-shot Anomaly Detection (AD), performing AD without reference
images of the test objects. We propose a framework called CLIP-AD to leverage the zero …