Search Results for author:
Found 16 papers, 12 papers with code
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance
To enhance the interaction between alignment and fusion modules and ensure spectral concordance during reconstruction, we propose a Feature Aggregation module and an Attention Fusion module.
Hyperspectral Image Super-Resolution
Image Super-Resolution
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Unleashing Vecset Diffusion Model for Fast Shape Generation
To address these challenges, we present FlashVDM, a systematic framework for accelerating both VAE and DiT in VDM.
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint.
MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost
In this work, we explore a cost-effective framework for multilingual image generation.
Scaling Mesh Generation via Compressive Tokenization
We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces.
FlexiTex: Enhancing Texture Generation with Visual Guidance
Recent texture generation methods achieve impressive results due to the powerful generative prior they leverage from large-scale text-to-image diffusion models.
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks
It not only allows flexible transmission of task information and gradient feedback between the MLLM and multiple downstream decoders but also effectively resolves training conflicts in multi-tasking scenarios.
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.
