  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. Unlike traditional
methods that mainly adopt fixed similarity measures for merging regions or
finding object proposals, our approach adaptively learns the region merging
similarity and the objectness measure during the process of hierarchical region
grouping. Specifically, guided by a structured loss, the ReNN model jointly
optimizes the cross-region similarity metric with the region merging process as
well as the objectness prediction. During inference of the object proposal
generation, we introduce randomness into the greedy search to cope with the
ambiguity of grouping regions. Extensive experiments on standard benchmarks,
e.g., PASCAL VOC and ImageNet, suggest that our approach is capable of
producing object proposals with high recall while well preserving the object
boundaries and outperforms other existing methods in both accuracy and
efficiency.
