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 …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

As the most fundamental scene understanding tasks, object detection and segmentation
have made tremendous progress in deep learning era. Due to the expensive manual …

Instance segmentation is data-hungry and as model capacity increases data scale becomes
crucial for improving the accuracy. Most instance segmentation datasets today require costly …

The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

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

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light
of the widespread adoption of vision-based foundational models. Its primary objective is to …

In-context segmentation has drawn more attention with the introduction of vision foundation
models. Most existing approaches adopt metric learning or masked image modeling to build …

Due to the global population increase and the recovery of agricultural demand after the
COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural …

Recent advances in generative models, such as diffusion models, have made generating
high-quality synthetic images widely accessible. Prior works have shown that training on …