2 code implementations • 22 Nov 2021 • Yanghao Li, Saining Xie, Xinlei Chen, Piotr Dollar, Kaiming He, Ross Girshick
The complexity of object detection methods can make this benchmarking non-trivial when new architectures, such as Vision Transformer (ViT) models, arrive.
2 code implementations • 18 Nov 2015 • Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
Beyond classification, we further validate the saliency of the learnt representations via their attribute concentration and hierarchy recovery properties, achieving 10-25% relative gains on the softmax classifier and 25-50% on triplet loss in these tasks.
2 code implementations • NeurIPS 2015 • Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar
Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier.
18 code implementations • 1 Apr 2015 • Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick
In this paper we describe the Microsoft COCO Caption dataset and evaluation server.
no code implementations • NeurIPS 2014 • Woonhyun Nam, Piotr Dollar, Joon Hee Han
In fact, orthogonal trees with our locally decorrelated features outperform oblique trees trained over the original features at a fraction of the computational cost.
Ranked #32 on Pedestrian Detection on Caltech
no code implementations • CVPR 2014 • Bharath Hariharan, C. L. Zitnick, Piotr Dollar
Several popular and effective object detectors separately model intra-class variations arising from deformations and appearance changes.
no code implementations • 27 May 2014 • Zhuowen Tu, Piotr Dollar, Ying-Nian Wu
Many the solutions to the problem require to perform logic operations such as `and', `or', and `not'.
no code implementations • CVPR 2013 • Dennis Park, C. L. Zitnick, Deva Ramanan, Piotr Dollar
We describe novel but simple motion features for the problem of detecting objects in video sequences.
no code implementations • CVPR 2013 • Joseph J. Lim, C. L. Zitnick, Piotr Dollar
Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images.