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24 April 2024 |
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segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection | | Yukun Zhu
; Raquel Urtasun
; Ruslan Salakhutdinov
; Sanja Fidler
; | | Date: |
15 Feb 2015 | | Abstract: | In this paper, we propose an approach that exploits object segmentation in
order to improve the accuracy of object detection. We frame the problem as
inference in a Markov Random Field, in which each detection hypothesis scores
object appearance as well as contextual information using Convolutional Neural
Networks, and allows the hypothesis to choose and score a segment out of a
large pool of accurate object segmentation proposals. This enables the detector
to incorporate additional evidence when it is available and thus results in
more accurate detections. Our experiments show an improvement of 4.1% in mAP
over the R-CNN baseline on PASCAL VOC 2010, and 3.4% over the current
state-of-the-art, demonstrating the power of our approach. | | Source: | arXiv, 1502.4275 | | Services: | Forum | Review | PDF | Favorites | |
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