Wisdom of (Binned) Crowds - [ACM Multimedia 2021]

A Bayesian Stratification Paradigm for Crowd Counting.

The 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.

If you want our work to be listed as a network for comparison, please send a pull request to us here. The instructions for pull request is mentioned here.

Please cite our paper if you end up using it for your own research.

                        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},