The chiral nature of the top quark couplings can be modified by new physics. Hence, the polarization of top quarks serves as a promising window for exploring the existence and nature of new physics. Detecting the polarization of boosted top quark jets in colliders is quite challenging, and several studies have already explored different kinematic variables for this purpose. In my talk I will describe an image-based neural network that can be used to measure the polarization of both leptonic and hadronic boosted top jets, and compare its performance with the typical kinematic polarimeter variables. In addition, the reconstruction of leptons inside highly boosted leptonically decaying top jets can be quite tricky. I will also discuss the tagging of leptonic (and hadronic) top jets using jet images, which is independent of the issues associated with lepton reconstruction in a very busy environment.
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