Region-based convolutional neural networks (R-CNN) fast_rcnn,faster_rcnn,mask_rcnn have largely dominated object detection. Operators defined on RoIs (Region of Interests) play an important role in R-CNNs such as RoIPooling fast_rcnn and RoIAlign mask_rcnn . They all only utilize information inside RoIs for RoI prediction, even with their recent deformable extensions deformable_cnn . Although surrounding context is well-known for its importance in object detection, it has yet been integrated in R-CNNs in a flexible and effective way. Inspired by the auto-context work auto_context and the multi-class object layout work nms_context , this paper presents a generic context-mining RoI operator (i.e., ) seamlessly integrated in R-CNNs, and the resulting object detection system is termed which is trained end-to-end. The proposed RoICtxMining operator is a simple yet effective two-layer extension of the RoIPooling or RoIAlign operator. Centered at an object-RoI, it creates a @math layout to mine contextual information adaptively in the @math surrounding context regions on-the-fly. Within each of the @math context regions, a context-RoI is mined in term of discriminative power and its RoIPooling RoIAlign features are concatenated with the object-RoI for final prediction. In experiments, it is evaluated using RoIPooling as the backbone and shows competitive results on Pascal VOC, Microsoft COCO, and KITTI datasets (including @math mAP improvements over the R-FCN rfcn method on COCO dataset and the first place on both KITTI pedestrian and cyclist detection as of this submission).
The role of context has been well exploited in recognition and detection @cite_40 @cite_4 @cite_49 @cite_51 @cite_39 @cite_14 @cite_5 . Specifically, @cite_40 utilized the classification confidence as context information, and learn the context model in an iterative way, showing significant improvements over Conditional Random Fields (CRFs) and Belief Propagation (BP) on context modeling. @cite_4 proposed to model the object-object semantic relationship as context in a @math cells, leading to improved performance over deformable part-based models (DPM) @cite_3 on PASCAL VOC dataset.
@cite_14 @cite_4 @cite_3 @cite_39 @cite_40 @cite_49 @cite_5 @cite_51
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In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of exist ing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales. Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-called (1st-order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector. The statistics of the 1st-order binary context features are further calculated to construct a high order co-occurrence descriptor. Combining the MOCO feature with the original image feature, we can evolve the baseline object detector to a stronger context aware detector. With the updated detector, we can continue the evolution till the contextual improvements saturate. Using the successful deformable-part-model detector [13] as the baseline detector, we test the proposed MOCO evolution framework on the PASCAL VOC 2007 dataset [8] and Caltech pedestrian dataset [7]: The proposed MOCO detector outperforms all known state-of-the-art approaches, contextually boosting deformable part models (ver. 5) [13] by 3.3 in mean average precision on the PASCAL 2007 dataset. For the Caltech pedestrian dataset, our method further reduces the log-average miss rate from 48 to 46 and the miss rate at 1 FPPI from 25 to 23 , compared with the best prior art [6]. In this paper, we focus on improving the proposal classification stage in the object detection task and present implicit negative sub-categorization and sink diversion to lift the performance by strengthening loss function in this stage. First, based on the observation that the “background” class is generally very diverse and thus challenging to be handled as a single indiscriminative class in existing state-of-the-art methods, we propose to divide the background category into multiple implicit sub-categories to explicitly differentiate diverse patterns within it. Second, since the ground truth class inevitably has low-value probability scores for certain images, we propose to add a “sink” class and divert the probabilities of wrong classes to this class when necessary, such that the ground truth label will still have a higher probability than other wrong classes even though it has low probability output. Additionally, we propose to use dilated convolution, which is widely used in the semantic segmentation task, for efficient and valuable context information extraction. Extensive experiments on PASCAL VOC 2007 and 2012 data sets show that our proposed methods based on faster R-CNN implementation can achieve state-of-the-art mAPs, i.e., 84.1 , 82.6 , respectively, and obtain 2.5 improvement on ILSVRC DET compared with that of ResNet. This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [19] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations.