CountCLIP -- [Re] Teaching CLIP to Count to Ten
<|reference_start|>CountCLIP -- [Re] Teaching CLIP to Count to Ten: Large vision-language models (VLMs) are shown to learn rich joint image-text representations enabling high performances in relevant downstream tasks. However, they fail to showcase their quantitative understanding of objects, and they lack good counting-aware representation. This paper conducts a reproducibility study of 'Teaching CLIP to Count to Ten' (Paiss et al., 2023), which presents a method to finetune a CLIP model (Radford et al., 2021) to improve zero-shot counting accuracy in an image while maintaining the performance for zero-shot classification by introducing a counting-contrastive loss term. We improve the model's performance on a smaller subset of their training data with lower computational resources. We verify these claims by reproducing their study with our own code. The implementation can be found at https://github.com/SforAiDl/CountCLIP.<|reference_end|>
@article{mestha2024countclip,
    title={CountCLIP -- [Re] Teaching CLIP to Count to Ten},
    author={Harshvardhan Mestha, Tejas Agrawal, Karan Bania, Shreyas V, Yash
  Bhisikar},
    journal={arXiv preprint arXiv:2406.03586},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03586},
    primaryClass={cs.CV cs.AI cs.LG}
}