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MLP Architectures for Vision-and-Language Modeling: An Empirical Study
[article]
2021
arXiv
pre-print
We initiate the first empirical study on the use of MLP architectures for vision-and-language (VL) fusion. Through extensive experiments on 5 VL tasks and 5 robust VQA benchmarks, we find that: (i) Without pre-training, using MLPs for multimodal fusion has a noticeable performance gap compared to transformers; (ii) However, VL pre-training can help close the performance gap; (iii) Instead of heavy multi-head attention, adding tiny one-head attention to MLPs is sufficient to achieve comparable
arXiv:2112.04453v1
fatcat:pnr5aeiwlffzncin4vdi5wojsi