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A Spitting Image: Modular Superpixel Tokenization in Vision Transformers

Marius Aasan·Odd Kolbjørnsen·Anne Schistad Solberg·Adín Ramirez Rivera
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Abstract

Vision Transformer (ViT) architectures traditionally employ a grid-based approach to tokenization independent of the semantic content of an image. We propose a modular superpixel tokenization strategy which decouples tokenization and feature extraction; a shift from contemporary approaches where these are treated as an undifferentiated whole. Using on-line content-aware tokenization and scale- and shape-invariant positional embeddings, we perform experiments and ablations that contrast our approach with patch-based tokenization and randomized partitions as baselines. We show that our method significantly improves the faithfulness of attributions, gives pixel-level granularity on zero-shot unsupervised dense prediction tasks, while maintaining predictive performance in classification tasks. Our approach provides a modular tokenization framework commensurable with standard architectures, extending the space of ViTs to a larger class of semantically-rich models.

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Marius Aasan, Odd Kolbjørnsen, Anne Schistad Solberg, Adín Ramirez Rivera. 2024-08-14. A Spitting Image: Modular Superpixel Tokenization in Vision Transformers. https://doi.org/10.1007/978-3-031-93806-1_11

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