<p>Answer to your question: Yes, they can. They can be slower and they can be faster than classic descriptors. For example, using only a single filter and several max-poolings will almost certainly be faster. But the results will also certainly be crappy.</p>

<p>You should ask a much more specific question. Relevant parts are:</p>

<ul>
<li><strong>Problem</strong>: Classification / Detection / Semantic Segmentation / Instance Segmentation / Face verification / ... ?</li>
<li><strong>Constraints</strong>: Minimum accuracy / maximum speed / maximum latency?</li>
<li><strong>Evaluation specifics</strong>:

<ul>
<li>Which hardware is available (GPUs)?</li>
<li>Do you evaluate on a single image? Often you can evaluate up to 512 images in about the same time as one image.</li>
</ul></li>
</ul>

<p>Also: The input image size should not be relevant. If CNNs achieve better results on smaller inputs than classic descriptors, why should you care?</p>

<h2>Papers</h2>

<p>Please note that CNNs are usually not tweaked towards speed, but towards accuracy.</p>

<ul>
<li>Detection: <a href="http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks" rel="nofollow noreferrer">Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks</a>: 600px x ~800px in 200ms on a GPU</li>
<li><a href="https://arxiv.org/pdf/1703.10956.pdf" rel="nofollow noreferrer">InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image</a>: 9.79ms with GeForce GTX Titan and AlexNet to get FC7 features</li>
<li>Semantic segmentation: <a href="https://arxiv.org/pdf/1511.00513.pdf" rel="nofollow noreferrer">Pixel-wise Segmentation of Street with Neural
Networks</a> 20ms with GeForce GTX 980</li>
</ul>
Can CNNs be faster than classic descriptors?
<p><strong>Disclamer:</strong> I don't know almost nothing on CNNs and I have no idea where I could ask this.</p>

<p>My research is focused on high performance on computer vision applications. We generate codes representing an image in less than 20 ms on images with the largest size of 500pxs. </p>

<p>This is done by combining SURF descriptors and <a href="http://www.vlfeat.org/overview/encodings.html" rel="nofollow noreferrer">VLAD</a> codes, obtaining a vector representing an image that will be used in our object recognition application. </p>

<p>Can CNNs be faster? According to <a href="https://github.com/soumith/convnet-benchmarks/blob/master/README.md" rel="nofollow noreferrer">this</a> benchmark (which is based on much smaller images) the times needed is longer, almost double considering that the size of the image is half of ours. </p>