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Found 4 papers, 1 papers with code
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning
Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream tasks utilizing pretrained diffusion models, such as domain translation and text-guided image manipulation.
SimCol3D -- 3D Reconstruction during Colonoscopy Challenge
We show that depth prediction in virtual colonoscopy is robustly solvable, while pose estimation remains an open research question.
Deep Cross-Modal Steganography Using Neural Representations
The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions.
Lightweight Monocular Depth Estimation via Token-Sharing Transformer
While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently.
