Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation

• URL: http://arxiv.org/abs/2404.14657v1
• Date: Tue, 23 Apr 2024 01:34:20 GMT
• Title: Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation
• Authors: Abhishek Aich, Yumin Suh, Samuel Schulter, Manmohan Chandraker,
• Abstract summary: A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. 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. We propose PRO-SCALE to reduce computations by a large margin with minimal sacrifice in performance.
• Score: 67.85309547416155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-style segmentation architectures 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.

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