Search Results for author:
Found 9 papers, 7 papers with code
ControlLLM: Augment Language Models with Tools by Searching on Graphs
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks.
Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models
The revolution of artificial intelligence content generation has been rapidly accelerated with the booming text-to-image (T2I) diffusion models.
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling
In this paper, we propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a recently-developed denoising diffusion generative model.
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2.
Hybrid Spectral Denoising Transformer with Guided Attention
Challenges in adapting transformer for HSI arise from the capabilities to tackle existing limitations of CNN-based methods in capturing the global and local spatial-spectral correlations while maintaining efficiency and flexibility.
Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance
The features of RGB reference images are then processed by a multi-stage alignment module to explicitly align the features of RGB reference with the LR HSI.
Mixed Attention Network for Hyperspectral Image Denoising
However, existing methods show limitations in exploring the spectral correlations across different bands and feature interactions within each band.
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
Hyperspectral image is unique and useful for its abundant spectral bands, but it subsequently requires extra elaborated treatments of the spatial-spectral correlation as well as the global correlation along the spectrum for building a robust and powerful HSI restoration algorithm.
Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenever the specifics of task changes.
