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GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in . . . SEM-CLIP requires little annotated data, substantially reducing labor demands in the semiconductor industry Extensive experimental validation demonstrates that our model achieves impressive classification and segmentation results under few-shot learning scenarios
[2411. 04997] LLM2CLIP: Powerful Language Model Unlocks Richer Visual . . . Motivated by the remarkable advancements in large language models (LLMs), this work explores how LLMs' superior text understanding and extensive open-world knowledge can enhance CLIP's capability, especially for processing longer and more complex image captions
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs
[2404. 16030] MoDE: CLIP Data Experts via Clustering - arXiv. org The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data are retrieved from publicly available datasets, and we pretrain Chinese CLIP models on the new dataset
Long-CLIP: Unlocking the Long-Text Capability of CLIP To this end, we propose Long-CLIP as a plug-and-play alternative to CLIP that supports long-text input, retains or even surpasses its zero-shot generalizability, and aligns the CLIP latent space, making it readily replace CLIP without any further adaptation in downstream frameworks