Hugging Face Trending Papers
Jul 15

Fine-grained CLIP fine-tuning with self-annotated region alignment

Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the whole image to a text description. Considering the large data and computational burden in pre-training a vision-language model from scratch, a series of works aim to enhance the fine-grained ability of CLIP through a fine-tuning scheme.

arXiv Computer Vision
Oct 23

ProCLIP: Progressive Vision-Language Alignment via LLM-based Embedder

ProCLIP introduces a curriculum‑learning framework that progressively aligns a CLIP image encoder with an LLM‑based text embedder. It first distills CLIP’s text encoder knowledge into the LLM embedder, then fine‑tunes the image encoder via contrastive learning with self‑distillation, instance semantic alignment, and embedding structure alignment losses. The approach addresses CLIP’s token limit, lack of multilingual support, and preserves pre‑trained vision‑language alignment.

By Xiaoxing Hu, Kaicheng Yang, Ziyang Gong, Qi Ming, Zonghao Guo, Yu Tian, Xiang An, Ziyong Feng, Xue Yang