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Found 10 papers, 5 papers with code
Adversarial Identity Injection for Semantic Face Image Synthesis
Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern.
Towards Controllable Face Generation with Semantic Latent Diffusion Models
To address that, in this paper we propose a SIS framework based on a novel Latent Diffusion Model architecture for human face generation and editing that is both able to reproduce and manipulate a real reference image and generate diversity-driven results.
Semantic Image Synthesis via Class-Adaptive Cross-Attention
Thus, they tend to overlook global image statistics, ultimately leading to unconvincing local style editing and causing global inconsistencies such as color or illumination distribution shifts.
Automatic Generation of Semantic Parts for Face Image Synthesis
Also, we show our model can be put before a SIS generator, opening the way to a fully automatic generation control of both shape and texture.
Memory-augmented Online Video Anomaly Detection
The ability to understand the surrounding scene is of paramount importance for Autonomous Vehicles (AVs).
Transferring Knowledge with Attention Distillation for Multi-Domain Image-to-Image Translation
Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks.
MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning
Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation.
MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization
In recent years, the majority of works on deep-learning-based image colorization have focused on how to make a good use of the enormous datasets currently available.
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval
The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks.
Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
It allows to drastically reduce the query time and outperforms the accuracy results compared to the state-of-the-art methods for large-scale landmark recognition.
