1. Figure 4. Testing minimal configurations with existing models for spatiotemporal recognition. (A-B) A binary classifier is trained to separate a positive set of similar minimal images (“rowing”), showing the same action at the same body region and viewing position (A) from a negative set (“not rowing”) including non-class images of the same size and style as the minimal configurations (B).
(C) One type of binary classifier was based on CNNs with 2D convolutional filters, followed by taking the maximum detection score from each frame. (D) Another type of binary classifier was based on CNNs with 3D convolutional filters (Duran et al., 2015;2018), which was fine-tuned with the positive and negative sets in A and B.
(E-G) The binary classifiers could not replicate human recognition, and performance by 3D and 2D CNNs was similar. Six example configurations that were misclassified including two of the same size (E), two temporally sub-minimal (F) and two spatially sub-minimal (G). ).