Specifically, inspired by the Masked Language Modeling (MLM) in NLP, we propose a masked token strategy based on the multi-head self-attention map, which dynamically masks some tokens of local patches without damaging the crucial structure for self-supervised learning. More importantly, the ...
🐛 Describe the bug Problem description The forward method of TransformerEncoderLayer provides an argument to pass in a mask to zero specific attention weights. However, the latter has no effect. Here is a minimal script to reproduce. Not...
Semantic SegmentationMapillary valMask2Former (Swin-L, multiscale)mIoU64.7# 3 Compare Semantic SegmentationMS COCOMaskFormer (Swin-L, single-scale)mIoU64.8# 5 Compare Semantic SegmentationMS COCOMask2Former (Swin-L, single-scale)mIoU67.4# 3 ...
Absolute Position Encodings • Adam • BPE • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer • ...
Whole Word Masked Input: the man [MASK] up , put his basket on [MASK] [MASK] [MASK] ' s head The training is identical -- we still predict each masked WordPiece token independently. The improvement comes from the fact that the original prediction task was too 'easy' for words that ha...
Our masked Transformer encoder is a multi-layer architecture in which each layer consists of a masked multi-head attention mechanism and a Feed-Forward Network (FFN). With the label embedding from the previous layer 𝐸𝑙−1El−1, each Transformer encoder layer exploits the label relationshi...
where a multi-head attention module is used to focus on important features from both branches and a convolution layer is used to reshape back to the original feature size of 𝐹𝑖𝑎Fai for a more straightforward decoding operation. Instead of pursuing the sole usage of the Swin Transformer...