Supplementary Material: The Role of Context for Object Detection and Semantic Segmentation in the Wild

In this paper [6], we are interested in analyzing the effect of context in detection and segmentation approaches. Towards this goal, we label every pixel of the training and validation sets of the PASCAL VOC 2010 detection challenge with a semantic class. We selected PASCAL as our testbed as it has served as the benchmark for detection and segmentation in the community for years (over 600 citations and tens of teams competing in the challenges each year). Our analysis shows that our new dataset is much more challenging than existing ones (e.g., Barcelona [7], SUN [8], SIFT flow [5]), as it has higher class entropy, less pixels are labeled as “stuff ” and instead belong to a wide variety of object categories beyond the 20 PASCAL object classes. We analyze the ability of state-of-the-art methods [7, 1] to perform semantic segmentation of the most frequent classes, and show that approaches based on nearest neighbor (NN) retrieval are significantly outperformed by approaches based on bottom-up grouping, showing the variability of PASCAL images. We also study the performance of contextual models for object detection, and show that existing models have a hard time dealing with PASCAL imagery. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. As contextual features we use class-specific segmentation features inspired by the success of segDPM [4]. We show that the model significantly helps in detecting objects at all scales and is particularly effective at tiny objects as well as extra-large ones