  Recently, convolutional neural networks (CNNs) with large size kernels have
attracted much attention in the computer vision field, following the success of
the Vision Transformers. Large kernel CNNs have been reported to perform well
in downstream vision tasks as well as in classification performance. The reason
for the high-performance of large kernel CNNs in downstream tasks has been
attributed to the large effective receptive field (ERF) produced by large size
kernels, but this view has not been fully tested. We therefore revisit the
performance of large kernel CNNs in downstream task, focusing on the weakly
supervised object localization (WSOL) task. WSOL, a difficult downstream task
that is not fully supervised, provides a new angle to explore the capabilities
of the large kernel CNNs. Our study compares the modern large kernel CNNs
ConvNeXt, RepLKNet, and SLaK to test the validity of the naive expectation that
ERF size is important for improving downstream task performance. Our analysis
of the factors contributing to high performance provides a different
perspective, in which the main factor is feature map improvement. Furthermore,
we find that modern CNNs are robust to the CAM problems of local regions of
objects being activated, which has long been discussed in WSOL. CAM is the most
classic WSOL method, but because of the above-mentioned problems, it is often
used as a baseline method for comparison. However, experiments on the
CUB-200-2011 dataset show that simply combining a large kernel CNN, CAM, and
simple data augmentation methods can achieve performance (90.99% MaxBoxAcc)
comparable to the latest WSOL method, which is CNN-based and requires special
training or complex post-processing. The code is available at
https://github.com/snskysk/CAM-Back-Again.
