We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.
More recently, several groups have combined object-proposal methods such as R-CNN @cite_25 @cite_3 with graphical models (e.g., @cite_34 ) or other CNN architectures (e.g., @cite_4 ) to combine object-to-object context with whole scene context in order to improve object detection.
@cite_34 @cite_4 @cite_25 @cite_3
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Object proposals have recently emerged as an essential cornerstone for object detection. The current state-of-the-art object detectors employ object proposals to detect objects within a modest set of candidate bounding box proposals instead of exhaustively searching across an image using the sliding window approach. However, achieving high recall and good localization with few proposals is still a challenging problem. The challenge becomes even more difficult in the context of autonomous driving, in which small objects, occlusion, shadows, and reflections usually occur. In this paper, we present a robust object proposals re-ranking algorithm that effectivity re-ranks candidates generated from a customized class-independent 3DOP (3D Object Proposals) method using a two-stream convolutional neural network (CNN). The goal is to ensure that those proposals that accurately cover the desired objects are amongst the few top-ranked candidates. The proposed algorithm, which we call DeepStereoOP, exploits not only RGB images as in the conventional CNN architecture, but also depth features including disparity map and distance to the ground. Experiments show that the proposed algorithm outperforms all existing object proposal algorithms on the challenging KITTI benchmark in terms of both recall and localization. Furthermore, the combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results of all three KITTI object classes. HighlightsWe present a robust object proposals re-ranking algorithm for object detection in autonomous driving.Both RGB images and depth features are included in the proposed two-stream CNN architecture called DeepStereoOP.Initial object proposals are generated from a customized class-independent 3DOP method.Experiments show that the proposed algorithm outperforms all existing object proposals algorithms.The combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results on KITTI benchmark. 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. Modern deep neural network-based object detection methods typically classify candidate proposals using their interior features. However, global and local surrounding contexts that are believed to be valuable for object detection are not fully exploited by existing methods yet. In this work, we take a step towards understanding what is a robust practice to extract and utilize contextual information to facilitate object detection in practice. Specifically, we consider the following two questions: “how to identify useful global contextual information for detecting a certain object?” and “how to exploit local context surrounding a proposal for better inferring its contents?” We provide preliminary answers to these questions through developing a novel attention to context convolution neural network (AC-CNN)-based object detection model. AC-CNN effectively incorporates global and local contextual information into the region-based CNN (e.g., fast R-CNN and faster R-CNN) detection framework and provides better object detection performance. It consists of one attention-based global contextualized (AGC) subnetwork and one multi-scale local contextualized (MLC) subnetwork. To capture global context, the AGC subnetwork recurrently generates an attention map for an input image to highlight useful global contextual locations, through multiple stacked long short-term memory layers. For capturing surrounding local context, the MLC subnetwork exploits both the inside and outside contextual information of each specific proposal at multiple scales. The global and local context are then fused together for making the final decision for detection. Extensive experiments on PASCAL VOC 2007 and VOC 2012 well demonstrate the superiority of the proposed AC-CNN over well-established baselines.