The last decade has seen enormous improvement for tasks such as object detection due to the creation of large annotated datasets which enable learning and benchmarking object detection algorithms. The goal of this workshop challenge is to measure the progress in image understanding as reflected in a diverse set of visual tasks, capturing both low- and high-level aspects of the vision problem. We thereby aspire to promote research that pushes the performance envelope in all facets of current computer vision research. To accomplish this we intend to systematize the evaluation of a large selection of representative visual tasks. This challenge provides a dataset with multiple-track detailed per-pixel labelings on PASCAL VOC images. For all datasets we will present baseline results for researchers to compare to. We expect that this will act like a catalyst for promoting research in multi-task learning, which we consider to be a key direction for future vision research.
For more details please visit the PASCAL in Detail Official Website.