We provide a platform on which researchers can investigate the performance of semantic part segmentation methods on challenging PASCAL VOC dataset. We build a benchmark, together with an evaluate server. The benchmark currently uses 7 articulated categories.
Free-fiewing fixations on a subset of 850 images from PASCAL VOC. Collected on 8 subjects, 3s viewing time, Eyelink II eye tracker. The performance of most algorithms suggest that PASCAL-S is less biased than most of the saliency datasets.
This dataset is a set of additional annotations for PASCAL VOC 2010. It goes beyond the original PASCAL object detection task by providing segmentation masks for each body part of the object. For categories that do not have a consistent set of parts (e.g., boat), we provide the silhouette annotation.
This dataset is a set of additional annotations for PASCAL VOC 2010. It goes beyond the original PASCAL semantic segmentation task by providing annotations for the whole scene (with 400+ labels). Every pixel has a unique class label. Instance information (i.e, different masks to separate different instances of the same class in the same image) are currently provided for the 20 PASCAL objects.