Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective

2024-11-05

AI Summary

To address the limited interpretability, low computational efficiency, and insufficient theoretical foundations of Transformers in semantic segmentation, this paper proposes DEPICT—a novel decoder grounded in principal component analysis (PCA). DEPICT is the first to formulate semantic segmentation as a low-rank reconstruction problem, enabling a fully attention-based architecture with theoretically justified design principles. It comprises three key components: embedding-refinement self-attention, class-aware cross-attention, and dot-product mask generation—collectively endowing both self- and cross-attention mechanisms with explicit, semantically meaningful principal component interpretations. This yields a transparent, interpretable “white-box” decoder. On ADE20K, DEPICT surpasses the black-box baseline Segmenter despite using fewer parameters, while demonstrating superior robustness. These results validate the effectiveness and generalizability of low-rank compression–inspired decoder design for semantic segmentation.

Technology Category

Computer Vision: Interpretability, Explainability, and TransparencyMachine Learning: Deep Generative Models & AutoencodersNatural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semanticsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSecurity and Privacy: Data transparency and provenance

Abstract

State-of-the-art methods for Transformer-based semantic segmentation typically adopt Transformer decoders that are used to extract additional embeddings from image embeddings via cross-attention, refine either or both types of embeddings via self-attention, and project image embeddings onto the additional embeddings via dot-product. Despite their remarkable success, these empirical designs still lack theoretical justifications or interpretations, thus hindering potentially principled improvements. In this paper, we argue that there are fundamental connections between semantic segmentation and compression, especially between the Transformer decoders and Principal Component Analysis (PCA). From such a perspective, we derive a white-box, fully attentional DEcoder for PrIncipled semantiC segemenTation (DEPICT), with the interpretations as follows: 1) the self-attention operator refines image embeddings to construct an ideal principal subspace that aligns with the supervision and retains most information; 2) the cross-attention operator seeks to find a low-rank approximation of the refined image embeddings, which is expected to be a set of orthonormal bases of the principal subspace and corresponds to the predefined classes; 3) the dot-product operation yields compact representation for image embeddings as segmentation masks. Experiments conducted on dataset ADE20K find that DEPICT consistently outperforms its black-box counterpart, Segmenter, and it is light weight and more robust.

Problem

• Image Classification
• Transformer Efficiency
• Region Segmentation

Innovation

• Transformer Decoder
• Principal Component Analysis (PCA)
• Efficient Image Segmentation