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

Dense retrieval (DR) converts queries and documents into dense embeddings and
measures the similarity between queries and documents in vector space. One of the major …

Transformer-based architectures have become the de-facto standard models for diverse
vision tasks owing to their superior performance. As the size of these transformer-based …

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 …

Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across
a wide range of domains, with increasing emphasis on enhancing their zero-shot …

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

With the scale of vision Transformer-based models continuing to grow, finetuning these
large-scale pretrained models for new tasks has become increasingly parameter-intensive …

Large transformer-based foundation models have been commonly used as pre-trained
models that can be adapted to different challenging datasets and settings with state-of-the …

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 …