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Found 6 papers, 0 papers with code
EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models
Our method formulates the task as a stochastic Brownian bridge process, a diffusion process with a fixed initial point as structure control and translates into the corresponding photo-realistic image while being conditioned solely on the given exemplar image.
Probabilistic Prompt Learning for Dense Prediction
Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models.
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution
We show that the proposed method produces visually diverse and plausible results in multiple domains compared to the state-of-the-art methods.
Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation
We address the problem of few-shot semantic segmentation (FSS), which aims to segment novel class objects in a target image with a few annotated samples.
Memory-guided Unsupervised Image-to-image Translation
We present a novel unsupervised framework for instance-level image-to-image translation.
Semantic Attribute Matching Networks
SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences.
