Learning to Segment Object Candidates via Recursive Neural Networks

4 Dec 2016 Tianshui Chen Liang Lin Xian Wu Nong Xiao Xiaonan Luo

To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effective approach for segmenting object proposals via a deep architecture of recursive neural networks (ReNNs), which hierarchically groups regions for detecting object candidates over scales... (read more)

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