🤖 AI Summary
This study addresses the computational and interpretability limitations of Transformers by systematically reviewing and categorizing forty non-attention-based vision models, including architectures based on convolutional networks, multilayer perceptrons (MLPs), and state space models. It establishes, for the first time, a unified taxonomy for non-Transformer visual approaches and provides a comprehensive evaluation across four key dimensions: efficiency, scalability, interpretability, and robustness. The analysis elucidates the respective strengths and weaknesses of each model class, clarifies their competitive standing across multiple performance metrics, and identifies both current challenges and promising directions for future research. This work thus offers a foundational framework and strategic guidance for advancing efficient and interpretable vision models.
📝 Abstract
Recently computer vision has seen advancements mainly thanks to Transformer-based models. However many non-Transformer methods are still doing well being a direct competition of Transformer-based models. This review tries to present a comprehensive taxonomy of such methods and organize these methods into categories like convolution-based models, MLP-based models, state-space-based and more. These methods are looked at in terms of how efficient they are, how well they scale, how easy they are to understand and how robust they are. A total of 40 papers were chosen for this study. The goal is to give a view of non-Transformer methods and find out what challenges and opportunities exist for future computer vision research.