Leveraging social media for training object detectors

The fact that most users tend to tag images emotionally rather than realistically makes social datasets inherently flawed from a computer vision perspective. On the other hand they can be particularly useful due to their social context and their potential to grow arbitrary big. Our work shows how a combination of techniques operating on both tag and visual information spaces, manages to leverage the associated weak annotations and produce region-detail training samples. In this direction we make some theoretical observations relating the robustness of the resulting models, the accuracy of the analysis algorithms and the amount of processed data. Experimental evaluation performed against manually trained object detectors reveals the strengths and weaknesses of our approach.