GCV23
* *Generative Models for Computer Vision
* Benchmarking Robustness to Text-Guided Corruptions
* Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion
* DeSRF: Deformable Stylized Radiance Field
* Diffusion-Enhanced PatchMatch: A Framework for Arbitrary Style Transfer with Diffusion Models
* Discovering Class-Specific GAN Controls for Semantic Image Synthesis
* Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion
* Explore the Power of Synthetic Data on Few-shot Object Detection
* Exploring Compositional Visual Generation with Latent Classifier Guidance
* Face Animation with an Attribute-Guided Diffusion Model
* Face Transformer: Towards High Fidelity and Accurate Face Swapping
* GAN-based Vision Transformer for High-Quality Thermal Image Enhancement
* Generating Adversarial Attacks in the Latent Space
* Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
* Improving Normalizing Flows with the Approximate Mass for Out-of-Distribution Detection
* Internal Diverse Image Completion
* Leveraging GANs for data scarcity of COVID-19: Beyond the hype
* Look ATME: The Discriminator Mean Entropy Needs Attention
* One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
* Scene Graph Driven Text-Prompt Generation for Image Inpainting
* Universal Guidance for Diffusion Models
* Unsupervised Bidirectional Style Transfer Network using Local Feature Transform Module
* Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image
* Vision + Language Applications: A Survey
24 for GCV23