Inverting Visual Representations with Detection Transformers

• URL: http://arxiv.org/abs/2412.06534v1
• Date: Mon, 09 Dec 2024 14:43:06 GMT
• Title: Inverting Visual Representations with Detection Transformers
• Authors: Jan Rathjens, Shirin Reyhanian, David Kappel, Laurenz Wiskott,
• Abstract summary: We apply the approach of training inverse models to reconstruct input images from intermediate layers within a Detection Transformer. We demonstrate critical properties of Detection Transformers, including contextual shape robustness, inter-layer correlation, and preservation to color perturbations.
• Score: 0.8124699127636158
• License:
• Abstract: Understanding the mechanisms underlying deep neural networks in computer vision remains a fundamental challenge. While many prior approaches have focused on visualizing intermediate representations within deep neural networks, particularly convolutional neural networks, these techniques have yet to be thoroughly explored in transformer-based vision models. In this study, we apply the approach of training inverse models to reconstruct input images from intermediate layers within a Detection Transformer, showing that this approach is efficient and feasible for transformer-based vision models. Through qualitative and quantitative evaluations of reconstructed images across model stages, we demonstrate critical properties of Detection Transformers, including contextual shape preservation, inter-layer correlation, and robustness to color perturbations, illustrating how these characteristics emerge within the model's architecture. Our findings contribute to a deeper understanding of transformer-based vision models. The code for reproducing our experiments will be made available at github.com/wiskott-lab/inverse-detection-transformer.

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