![]() Patterson, G., Hays, J.: SUN attribute database: Discovering, annotating, and recognizing scene attributes. In: ICLR (April 2014)įarhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. Sermanet, P., Eigen, D., Zhang, S., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: Integrated recognition, localization and detection using convolutional networks. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: CVPR (2010)ĭollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: SUN database: Large-scale scene recognition from abbey to zoo. In: CVPR (2009)Įveringham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. ![]() This process is experimental and the keywords may be updated as the learning algorithm improves.ĭeng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. These keywords were added by machine and not by the authors. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. ![]() With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. Objects are labeled using per-instance segmentations to aid in precise object localization. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. ![]() We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. ![]()
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