ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding and Segmentation

• URL: http://arxiv.org/abs/2411.12589v2
• Date: Sat, 22 Mar 2025 19:54:49 GMT
• Title: ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding and Segmentation
• Authors: Hesam Hosseini, Ghazal Hosseini Mighan, Amirabbas Afzali, Sajjad Amini, Amir Houmansadr,
• Abstract summary: We introduce ULTra, a framework for interpreting Transformer embeddings and uncovering meaningful semantic patterns within them.<n>We propose a self-supervised training approach that refines segmentation performance by learning an external transformation matrix without modifying the underlying model.<n>We validate ULTra for model interpretation on both synthetic and real-world scenarios, including Object Selection and interpretable text summarization.
• Score: 14.84547724351634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers have revolutionized Computer Vision (CV) through self-attention mechanisms. However, their complexity makes latent token representations difficult to interpret. We introduce ULTra, a framework for interpreting Transformer embeddings and uncovering meaningful semantic patterns within them. ULTra enables unsupervised semantic segmentation using pre-trained models without requiring fine-tuning. Additionally, we propose a self-supervised training approach that refines segmentation performance by learning an external transformation matrix without modifying the underlying model. Our method achieves state-of-the-art performance in unsupervised semantic segmentation, outperforming existing segmentation methods. Furthermore, we validate ULTra for model interpretation on both synthetic and real-world scenarios, including Object Selection and interpretable text summarization using LLMs, demonstrating its broad applicability in explaining the semantic structure of latent token representations.

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