Official ICC model

The official checkpoint of ICC model, introduced in ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation

Project Page

Usage

The ICC model is used to quantify the concreteness of image captions (and sentences in general).

Running the model

Click to expand

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("moranyanuka/icc")
model = AutoModelForSequenceClassification.from_pretrained("moranyanuka/icc").to("cuda")

captions = ["a great method of quantifying concreteness", "a man with a white shirt"]
text_ids = tokenizer(captions, padding=True, return_tensors="pt", truncation=True).to('cuda')
with torch.inference_mode():
  icc_scores = model(**text_ids)['logits']

# tensor([[0.0339], [1.0068]])

bibtex:

@misc{yanuka2024icc,
      title={ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation}, 
      author={Moran Yanuka and Morris Alper and Hadar Averbuch-Elor and Raja Giryes},
      year={2024},
      eprint={2403.01306},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}