Search Results for author: Piotr Dollar

Found 9 papers, 4 papers with code

Benchmarking Detection Transfer Learning with Vision Transformers

2 code implementations22 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.

Benchmarking object-detection +3

Metric Learning with Adaptive Density Discrimination

2 code implementations18 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.

Attribute Classification +3

Learning to Segment Object Candidates

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.

Object object-detection +4

Local Decorrelation For Improved Pedestrian Detection

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.

object-detection Object Detection +1

Detecting Objects using Deformation Dictionaries

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.


Layered Logic Classifiers: Exploring the `And' and `Or' Relations

no code implementations27 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'.

Pedestrian Detection Semantic Segmentation +1

Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection

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.

Contour Detection object-detection +1

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