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Found 5 papers, 4 papers with code
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e. g., LAION).
Magic-Me: Identity-Specific Video Customized Diffusion
To achieve this, we propose three novel components that are essential for high-quality identity preservation and stable video generation: 1) a noise initialization method with 3D Gaussian Noise Prior for better inter-frame stability; 2) an ID module based on extended Textual Inversion trained with the cropped identity to disentangle the ID information from the background 3) Face VCD and Tiled VCD modules to reinforce faces and upscale the video to higher resolution while preserving the identity's features.
Debiased Learning for Remote Sensing Data
Deep learning has had remarkable success at analyzing handheld imagery such as consumer photos due to the availability of large-scale human annotations (e. g., ImageNet).
Meta-Personalizing Vision-Language Models to Find Named Instances in Video
Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications.
Decoupled Contrastive Learning
Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72. 3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning.
