OASIS VS VLDet

Conclusion So this is how StyleGAN-NADA, a CLIP-guided zero-shot method for Non-Adversarial Domain Adaptation of image generators, works. Although the StyleGAN-NADA is focused on StyleGAN, it can be applied to other generative architectures such as OASIS and many others.

We're excited to share our latest work "Learning Object-Language Alignments for Open-Vocabulary Object Detection", which got accepted to ICLR'2023. Here're some resources: arxiv paper: https://arxiv.org/abs/2211.14843 github: https://github.com/clin1223/VLDet The proposed method called **VLDet**, which is a a simple yet effective vision-and-language framework for open-vocabulary object detection. Our key efforts are: 🔥 We introduce an open-vocabulary object detector method to learn object-language alignments directly from image-text pair data. 🔥 We propose to formulate region-word alignments as a set-matching problem and solve it efficiently with the Hungarian algorithm. 🔥 We use all nouns from image-text pairs as our object voccabulary which is strictly following the open-vocabulary setting and extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance.