Learning Detection with Diverse Proposals

To predict a set of diverse and informative proposals with enriched representations, this paper introduces a differentiable Determinantal Point Process (DPP) layer that is able to augment the object detection architectures. Most modern object detection architectures, such as Faster R-CNN, learn to localize objects by minimizing deviations from the ground-truth but ignore correlation between multiple proposals and object categories... (read more)

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Methods used in the Paper


METHOD TYPE
RPN
Region Proposal
Softmax
Output Functions
Convolution
Convolutions
RoIPool
RoI Feature Extractors
Faster R-CNN
Object Detection Models