The paper "AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities" presents a method to train a strong bilingual/multilingual multimodal representation model by altering the text encoder of the pre-trained multimodal representation model CLIP with a pre-trained multilingual text encoder XLM-R, and aligning both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning, achieving state-of-the-art performances on various tasks.

Key insights and lessons learned:

• CLIP's pre-trained language encoder can be replaced with a pre-trained multilingual encoder to extend its language capabilities.
• The proposed two-stage training schema consisting of teacher learning and contrastive learning can effectively align both languages and image representations.
• The resulting AltCLIP model achieved state-of-the-art performances on various tasks, including image classification and captioning in multiple languages.

Questions for the authors:

1. How did you choose XLM-R as the pre-trained multilingual text encoder to replace CLIP's language encoder?
2. Could AltCLIP be extended to more than two languages? How would the training process change?
3. How sensitive is AltCLIP's performance to the choice of the pre-trained multilingual text encoder?
4. Can AltCLIP be fine-tuned on downstream tasks in a language-specific manner to further improve its performance?
5. What are the limitations of AltCLIP, and what future directions do you suggest to address them?

Future research directions:

1. Investigating the performance of AltCLIP on low-resource languages.
2. Exploring the transferability of AltCLIP to other multimodal tasks, such as visual question answering.
3. Adapting AltCLIP to handle more than two languages in a more efficient way.
4. Studying the impact of different pre-training objectives for the multilingual text encoder on AltCLIP's performance.
5. Investigating the robustness of AltCLIP to noisy and biased training data.

Relevant references:

1. Radford, A., et al. "Learning transferable visual models from natural language supervision." Advances in Neural Information Processing Systems. 2019.
2. Tan, H., et al. "Multilingual image captioning with visual attention." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.