text: Abstract The task of object viewpoint estimation has been a challenge since the early days of computer vision. To estimate the viewpoint (or pose) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, extrinsic elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been proven to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how information from other objects in the scene can be exploited for viewpoint estimation. In particular, we look at object configurations by following a relational neighbor-based approach for reasoning about object relations. We show that, starting from noisy object detections and viewpoint estimates, exploiting the estimated viewpoint and location of other objects in the scene can lead to improved object viewpoint predictions. Experiments on the KITTI dataset demonstrate that object configurations can indeed be used as a complementary cue to appearance-based viewpoint estimation. Our analysis reveals that the proposed context-based method can improve object viewpoint estimation by reducing specific types of viewpoint estimation errors commonly made by methods that only consider local information. Moreover, considering contextual information produces superior performance in scenes where a high number of object instances occur. Finally, our results suggest that, following a cautious relational neighbor formulation brings improvements over its aggressive counterpart for the task of object viewpoint estimation.
  @cite_35: Many collective classification (CC) algorithms have been shown to increase accuracy when instances are interrelated. However, CC algorithms must be carefully applied because their use of estimated labels can in some cases decrease accuracy. In this article, we show that managing this label uncertainty through cautious algorithmic behavior is essential to achieving maximal, robust performance. First, we describe cautious inference and explain how four well-known families of CC algorithms can be parameterized to use varying degrees of such caution. Second, we introduce cautious learning and show how it can be used to improve the performance of almost any CC algorithm, with or without cautious inference. We then evaluate cautious inference and learning for the four collective inference families, with three local classifiers and a range of both synthetic and real-world data. We find that cautious learning and cautious inference typically outperform less cautious approaches. In addition, we identify the data characteristics that predict more substantial performance differences. Our results reveal that the degree of caution used usually has a larger impact on performance than the choice of the underlying inference algorithm. Together, these results identify the most appropriate CC algorithms to use for particular task characteristics and explain multiple conflicting findings from prior CC research.
  @cite_7: The task of object pose estimation has been a challenge since the early days of computer vision. To estimate the pose (or viewpoint) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, external elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been shown to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how information from other objects in the scene can be exploited for pose estimation. In particular, we look at object configurations. We show that, starting from noisy object detections and pose estimates, exploiting the estimated pose and location of other objects in the scene can help to estimate the objects' poses more accurately. We explore both a camera-centered as well as an object-centered representation for relations. Experiments on the challenging KITTI dataset show that object configurations can indeed be used as a complementary cue to appearance-based pose estimation. In addition, object-centered relational representations can also assist object detection.
  @cite_36: Relational data offer a unique opportunity for improving the c lassification accuracy o f statistical m odels. If two objects are related, inferring something about one object can aid inferences about the other. We present an iterative classification p rocedure that exploits this characteristic of relational data. This approach uses simple Bayesian classifiers in an iterative fashion, dynamically upd ating the attributes of some objects as inferences are made about related ob jects. Inferences made with h igh confidence in initial iterations are fed back into the data and are used to inform subsequent i nferences about related ob jects. We evaluate the performance of this approach on a binary classification task. Experiments indicate that it erative classification significantly increases accuracy when compared to a single-pass approach.
  @cite_3: We present a data-driven approach to predict the importance of edges and construct a Markov network for image analysis based on statistical models of global and local image features. We also address the coupled problem of predicting the feature weights associated with each edge of a Markov network for evaluation of context. Experimental results indicate that this scene dependent structure construction model eliminates spurious edges and improves performance over fully-connected and neighborhood connected Markov network.
  @cite_59: 
  @cite_66: It is by now generally accepted that reasoning about the relationships between objects (and object hypotheses) can improve the accuracy of object detection methods. Relations between objects allow to reject inconsistent hypotheses and reduce the uncertainty of the initial hypotheses. However, most methods to date reason about object relations in a relatively crude way. In this paper we propose an alternative using cautious inference. Building on ideas from Collective Classification, we favor the most confident hypotheses as sources of contextual information and give higher relevance to the object relations observed during training. Additionally, we propose to cluster the pairwise relations into relationships. Our experiments on part of the KITTI data benchmark and the MIT StreetScenes dataset show that both steps improve the performance of relational classifiers.
summary: From the perspective of the Collective Classification literature @cite_59 , specifically on the inference side, our method is inspired by @cite_35 @cite_36 . This is a type of inference that seeks to identify and exploit the more certain relational information. Such a cautious approach was used in @cite_3 for labeling object superpixels. In @cite_3 , discriminative relations were mined between object regions and discriminative attributes were discovered per relation. In @cite_66 , relations between objects were considered to improve object detection by penalizing out-of-context object hypotheses. During inference, the object hypotheses with highest certainty are classified first and then used to bootstrap the other objects. Based on their experiments, it was concluded that all cases following this cautious iterative approach resulted in better object detection performance than when considering the neighboring objects at once. In this work, we start from the observations of @cite_66 , and explore a cautious counterpart of @cite_7 with the objective of verifying whether the observations made for object detection also hold for the task of object viewpoint estimation. To this end, we first estimate the viewpoint of the objects with higher certainty and then use these objects to predict the viewpoints of the other ones.