Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation
Keywords: universal segmentation; efficient transformers
TL;DR: We propose an efficient transformer encoder for universal segmentation architectures.
Abstract: A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency being a high priority for scaling such models, we observed that the state-of-the-art method Mask2Former uses \~50% of its compute only on the transformer encoder. This is due to the retention of a full-length token-level representation of all backbone feature scales at each encoder layer. With this observation, we propose a strategy termed PROgressive Token Length SCALing for Efficient transformer encoders (PRO-SCALE) that can be plugged-in to the Mask2Former segmentation architecture to significantly reduce the computational cost. The underlying principle of PRO-SCALE is: progressively scale the length of the tokens with the layers of the encoder. This allows PRO-SCALE to reduce computations by a large margin with minimal sacrifice in performance (\~52% GFLOPs reduction with no drop in performance on COCO dataset). We validate our framework on multiple public benchmarks. Our code will be publicly released.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 476
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