11fc8c98b46d4cbdfe8157267228f7d7-Supplemental-Conference.pdf

Table 6: Uni-Perceiver model variants used in this paper. Uni-Perceiver-B and Uni-Perceiver-L have the same architectures as their corresponding ViT variants, respectively. There are some setting changes to improve the training stability of the original Uni-Perceiver. The loss weights are adjusted to meet reasonable optimizations for all tasks by observing the early training losses through short-epoch experiments. Based on the above settings, we can train Uni-Perceiver more efficiently.