Search Results for author:
Found 8 papers, 5 papers with code
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model
To address this limitation, a super-resolution recurrent diffusion model (SRDM) has been developed to enhance the temporal resolution of climate data and model the short-term uncertainty.
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization
As a result, FACT solely learns identity preservation from training data, thereby minimizing the impact on the original text-to-image capabilities of the base model.
TopoFR: A Closer Look at Topology Alignment on Face Recognition
To address this problem, we propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE.
LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation
Diffusion models have exhibited substantial success in text-to-image generation.
CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image features and a Mamba-based decoder for aggregating and integrating global information, facilitating efficient semantic segmentation of remote sensing images.
FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio
In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and Imagining, into the task of high-fidelity diverse talking faces generation from a single audio.
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
To address this issue, we propose a robust contrastive-based S4 framework, termed the Probabilistic Representation Contrastive Learning (PRCL) framework to enhance the robustness of the unsupervised training process.
TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective
We investigate the reasons for this phenomenon and discover that the existing data augmentation approach and hard sample mining strategy are incompatible with ViTs-based FR backbone due to the lack of tailored consideration on preserving face structural information and leveraging each local token information.
