7 accepted papers
Video diffusion models have achieved impressive results in natural scene generation, yet they struggle to generalize to scientific phenomena such as fluid simulations and meteorological processes, where underlying dynamics are governed by scientific laws. These tasks pose unique challenges, includin
Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and un…
Time series forecasting (TSF) is essential in various domains, and recent advancements in diffusion-based TSF models have shown considerable promise. However, these models typically adopt traditional diffusion patterns, treating TSF as a noise-based conditional generation task. This approach neglect…
Foundational vision-language models (VLMs) like CLIP have revolutionized image recognition, but adapting them to specialized domains with limited data remains challenging. We propose Domain Prompt Learning with Quaternion Networks (DPLQ), which leverages domain-specific foundation models and quatern
Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However when adapting VLMs to specialized domains such as remote sensing and medical imaging domain prompt learning remains underexplored. While large-scale domain-specific foundation mod…
Large pre-trained vision-language models, such as CLIP, have shown remarkable generalization capabilities across various tasks when appropriate text prompts are provided. However, adapting these models to specific domains, like remote sensing images (RSIs), medical images, etc, remains unexplored an…
Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel frame…