he idea behind our work is to tackle the high variance of error that is ignored when considering de facto statistical performance measures like (MSE,MAE) for performance evaluation in the crowd counting domain. Our recipe involves finding strata that are optimal in a Bayesian sense and later systematically modifying the standard crowd counting pipeline to incorporate decrease of variance at each step.

 

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    @inproceedings{10.1145/3474085.3475522,
        author = {Sravya Vardhani Shivapuja, Mansi Pradeep Khamkar, Divij Bajaj, Ganesh Ramakrishnan, Ravi Kiran Sarvadevabhatla},
        title = {Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting},
        booktitle = {Proceedings of the 2021 ACM Conference on Multimedia},
        year = {2021},
        location = {Virtual Event, China},
        publisher = {ACM},
        address = {China},
        }