We introduce Multi-Expert Region-based CNN (ME R-CNN) which is equipped with multiple experts and built on top of the R-CNN framework known to be one of the state-of-the-art object detection methods. ME R-CNN focuses in better capturing the appearance variations caused by different shapes, poses, and viewing angles. The proposed approach consists of three experts each responsible for objects with particular shapes: horizontally elongated, square-like, and vertically elongated. On top of using selective search which provides a compact, yet effective set of region of interests (RoIs) for object detection, we augmented the set by also employing the exhaustive search for training only. Incorporating the exhaustive search can provide complementary advantages: i) it captures the multitude of neighboring RoIs missed by the selective search, and thus ii) provide significantly larger amount of training examples. We show that the ME R-CNN architecture provides considerable performance increase over the baselines on PASCAL VOC 07, 12, and MS COCO datasets.
Instead of going dense', some methods employed relatively sparse' searching approaches by introducing the concept of objectness. @cite_24 used an objectness quality function to discard sub-search spaces whose objectness scores are under a certain threshold, where object detector becomes an objectness quality function. Instead of using the object detector, @cite_19 introduces a generic objectness measure, to estimate how likely it is for a region to contain object of any category using saliency, color contrast, edge density, and boundary information. Several more approaches @cite_22 @cite_31 @cite_0 @cite_30 @cite_28 @cite_9 to generate RoIs based on objectness characteristics have been introduced afterwards. Recently, @cite_26 introduced a region proposal network (RPN) incorporated into the CNN which also generates RoIs based on the objectness.
@cite_30 @cite_26 @cite_22 @cite_28 @cite_9 @cite_24 @cite_19 @cite_0 @cite_31
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We present R-FCN-3000, a large-scale real-time object detector in which objectness detection and classification are decoupled. To obtain the detection score for an RoI, we multiply the objectness score with the fine-grained classification score. Our approach is a modification of the R-FCN architecture in which position-sensitive filters are shared across different object classes for performing localization. For fine-grained classification, these position-sensitive filters are not needed. R-FCN-3000 obtains an mAP of 34.9 on the ImageNet detection dataset and outperforms YOLO-9000 by 18 while processing 30 images per second. We also show that the objectness learned by R-FCN-3000 generalizes to novel classes and the performance increases with the number of training object classes - supporting the hypothesis that it is possible to learn a universal objectness detector. Code will be made available. Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that "objectness" is in fact a high level construct. We argue for a data-driven, semantic approach for ranking object proposals. Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method. We use a novel four-layer CNN architecture that is as good as much larger networks on the task of evaluating objectness while being much faster. We show that DeepBox significantly improves over the bottom-up ranking, achieving the same recall with 500 proposals as achieved by bottom-up methods with 2000. This improvement generalizes to categories the CNN has never seen before and leads to a 4.5-point gain in detection mAP. Our implementation achieves this performance while running at 260 ms per image. Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct. We argue for a data-driven, semantic approach for ranking object proposals. Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method. We use a novel four-layer CNN architecture that is as good as much larger networks on the task of evaluating objectness while being much faster. We show that DeepBox significantly improves over the bottom-up ranking, achieving the same recall with 500 proposals as achieved by bottom-up methods with 2000. This improvement generalizes to categories the CNN has never seen before and leads to a 4.5-point gain in detection mAP. Our implementation achieves this performance while running at 260 ms per image. The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7 on VOC07, 80.4 on VOC12, and 34.4 on COCO. Codes will be made publicly available.