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

Prompt learning has emerged as a valuable technique in enhancing vision-language
models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly …

With the continuous growth of large language models, the process of fine-tuning these
models for new tasks has become increasingly parameter-intensive. Prompt tuning, a …

We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …

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 …

As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning
(VPT) as a parameter-efficient transfer learning technique has gained attention due to its …

Addressing the challenges of high model complexity, low generalization capability, and
suboptimal detection performance in most algorithms for crop leaf disease detection, the …

Multimodal (eg, RGB-Depth/RGB-Thermal) fusion has shown great potential for improving
semantic segmentation in complex scenes (eg, indoor/low-light conditions). Existing …

Multiple instance learning (MIL)-based framework has become the mainstream for
processing the whole slide image (WSI) with giga-pixel size and hierarchical image context …

This paper addresses two vital challenges in Unsupervised Domain Adaptation (UDA) with a
focus on harnessing the power of Vision-Language Pre-training (VLP) models. Firstly, UDA …