  Previous work on multimodal machine translation (MMT) has focused on the way
of incorporating vision features into translation but little attention is on
the quality of vision models. In this work, we investigate the impact of vision
models on MMT. Given the fact that Transformer is becoming popular in computer
vision, we experiment with various strong models (such as Vision Transformer)
and enhanced features (such as object-detection and image captioning). We
develop a selective attention model to study the patch-level contribution of an
image in MMT. On detailed probing tasks, we find that stronger vision models
are helpful for learning translation from the visual modality. Our results also
suggest the need of carefully examining MMT models, especially when current
benchmarks are small-scale and biased. Our code could be found at
\url{https://github.com/libeineu/fairseq_mmt}.
