Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
This work addresses the challenge of scale generalization in CNNs for computer vision applications, but it is incremental as it builds on existing methods and highlights remaining limitations.
The authors tackled the problem of scale equivariance in convolutional neural networks by introducing the STIR benchmark and a new model family using re-scaled kernels with shared weights, showing improved generalization across scales compared to standard convolutions, though performance degrades for large scale differences.
The widespread success of convolutional neural networks may largely be attributed to their intrinsic property of translation equivariance. However, convolutions are not equivariant to variations in scale and fail to generalize to objects of different sizes. Despite recent advances in this field, it remains unclear how well current methods generalize to unobserved scales on real-world data and to what extent scale equivariance plays a role. To address this, we propose the novel Scaled and Translated Image Recognition (STIR) benchmark based on four different domains. Additionally, we introduce a new family of models that applies many re-scaled kernels with shared weights in parallel and then selects the most appropriate one. Our experimental results on STIR show that both the existing and proposed approaches can improve generalization across scales compared to standard convolutions. We also demonstrate that our family of models is able to generalize well towards larger scales and improve scale equivariance. Moreover, due to their unique design we can validate that kernel selection is consistent with input scale. Even so, none of the evaluated models maintain their performance for large differences in scale, demonstrating that a general understanding of how scale equivariance can improve generalization and robustness is still lacking.