Explore the challenges and solutions in computer vision models through this comprehensive tutorial from CVPR'22. Delve into standard visual recognition pipelines, benchmark performances, and dataset biases. Examine adversarial examples, benchmark challenges, and domain adaptation techniques. Learn about adapting to imbalanced data, self-training methods, and the SENTRY approach for selective entropy optimization. Investigate performance degradation from bias, geographic diversity in data, and the concept of responsible vision. Gain insights into making object recognition work effectively for everyone across different geographical contexts.