The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at addressing the problem of complex scene understanding lack representational power, efficiency, and the ability to create robust meta-knowledge about scenes. In this paper, we introduce scenarios as a new way of representing scenes. The scenario is a simple, low-dimensional, data-driven representation consisting of sets of frequently co-occurring objects and is useful for a wide range of scene understanding tasks. We learn scenarios from data using a novel matrix factorization method which we integrate into a new neural network architecture, the ScenarioNet. Using ScenarioNet, we can recover semantic information about real world scene images at three levels of granularity: 1) scene categories, 2) scenarios, and 3) objects. Training a single ScenarioNet model enables us to perform scene classification, scenario recognition, multi-object recognition, content-based scene image retrieval, and content-based image comparison. In addition to solving many tasks in a single, unified framework, ScenarioNet is more computationally efficient than other CNNs because it requires significantly fewer parameters while achieving similar performance on benchmark tasks and is more interpretable because it produces explanations when making decisions. We validate the utility of scenarios and ScenarioNet on a diverse set of scene understanding tasks on several benchmark datasets.
Other groups focus on using sets of objects to aid object detection. @cite_28 discovers groups of objects of arbitrary size, model these groups using deformable parts models, and directly detects these groups in images. @cite_10 constructs classifiers that operate over sets of objects using object-object and object-scene relations to re-score and remove noisy detections.
@cite_28 @cite_10
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Objects in scenes interact with each other in complex ways. A key observation is that these interactions manifest themselves as predictable visual patterns in the image. Discovering and detecting these structured patterns is an important step towards deeper scene understanding. It goes beyond using either individual objects or the scene as a whole as the semantic unit. In this work, we promote "groups of objects". They are high-order composites of objects that demonstrate consistent spatial, scale, and viewpoint interactions with each other. These groups of objects are likely to correspond to a specific layout of the scene. They can thus provide cues for the scene category and can also prime the likely locations of other objects in the scene. It is not feasible to manually generate a list of all possible groupings of objects we find in our visual world. Hence, we propose an algorithm that automatically discovers groups of arbitrary numbers of participating objects from a collection of images labeled with object categories. Our approach builds a 4-dimensional transform space of location, scale and viewpoint, and efficiently identifies all recurring compositions of objects across images. We then model the discovered groups of objects using the deformable parts-based model. Our experiments on a variety of datasets show that using groups of objects can significantly boost the performance of object detection and scene categorization. In this work we use Branch-and-Bound (BB) to efficiently detect objects with deformable part models. Instead of evaluating the classifier score exhaustively over image locations and scales, we use BB to focus on promising image locations. The core problem is to compute bounds that accommodate part deformations; for this we adapt the Dual Trees data structure [7] to our problem. We evaluate our approach using Mixture-of-Deformable Part Models [4]. We obtain exactly the same results but are 10-20 times faster on average. We also develop a multiple-object detection variation of the system, where hypotheses for 20 categories are inserted in a common priority queue. For the problem of finding the strongest category in an image this results in a 100-fold speedup. In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (, ECCV, 2012). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the @math gPb-ucm approach of (TPAMI, 2011) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (, TPAMI, 2010). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art. Object class detectors typically apply a window classifier to all the windows in a large set, either in a sliding window manner or using object proposals. In this paper, we develop an active search strategy that sequentially chooses the next window to evaluate based on all the information gathered before. This results in a substantial reduction in the number of classifier evaluations and in a more elegant approach in general. Our search strategy is guided by two forces. First, we exploit context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set. This enables to jump across distant regions in the image (e.g. observing a sky region suggests that cars might be far below) and is done efficiently in a Random Forest framework. Second, we exploit the score of the classifier to attract the search to promising areas surrounding a highly scored window, and to keep away from areas near low scored ones. Our search strategy can be applied on top of any classifier as it treats it as a black-box. In experiments with R-CNN on the challenging SUN2012 dataset, our method matches the detection accuracy of evaluating all windows independently, while evaluating 9× fewer windows.