This paper explores the impact of occlusions in video action detection. We facilitatethis study by introducing five new benchmark datasets namely O-UCF and O-JHMDB consisting of synthetically controlled static/dynamic occlusions, OVIS-UCF and OVIS-JHMDB consisting of occlusions with realistic motions and Real-OUCF for occlusions in realistic-world scenarios. We formally confirm an intuitiveexpectation: existing models suffer a lot as occlusion severity is increased andexhibit different behaviours when occluders are static vs when they are moving.We discover several intriguing phenomenon emerging in neural nets: 1) transformerscan naturally outperform CNN models which might have even used occlusion as aform of data augmentation during training 2) incorporating symbolic-componentslike capsules to such backbones allows them to bind to occluders never even seenduring training and 3) Islands of agreement (similar to the ones hypothesized inHinton et Al’s GLOM) can emerge in realistic images/videos without instance-levelsupervision, distillation or contrastive-based objectives(eg. video-textual training).Such emergent properties allow us to derive simple yet effective training recipeswhich lead to robust occlusion models inductively satisfying the first two stages ofthe binding mechanism (grouping/segregation). Models leveraging these recipesoutperform existing video action-detectors under occlusion by 32.3% on O-UCF,32.7% on O-JHMDB & 2.6% on Real-OUCF in terms of the vMAP metric. The code for this work has been released at https: //github.com/rajatmodi62/OccludedActionBenchmark.