No, this still wouldn’t work, as each output pixel position might be calculated by a larger field of the input activation(s).
If all your convolutions use a 1x1 kernel, the output pixel locations would correspond to the input locations.
Captum 3 uses specific methods for model interpretability, which e.g. use the gradient flow to visualize which activations were “important” for which output part, but that doesn’t seem to be your use case.