  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.
