Update conclusion

sections/conclusion_future_work/conclusion.md

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-In this project, we presented Proof-of-Concept with our CLIP Vision + BERT model baseline which leverages a multilingual checkpoint with pre-trained image encoders in four languages - **English, French, German, and Spanish**. We hope to improve this in the future by using better translators (for e.g. Google Translate API) to get more multilingual data, especially in low-resource languages.
+In this project, we presented Proof-of-Concept with our CLIP Vision + BERT model baseline which leverages a multilingual checkpoint with pre-trained image encoders in four languages - **English, French, German, and Spanish**. Our model performs very well considering the amount of training time we were able to get and achieves 0.49 eval accuracy on our multilingual VQAv2 dataset.