Learning object detectors requires massive amounts of labeled training samples from the specific data source of interest. This is impractical when dealing with many different sources (e.g., in camera networks), or constantly changing ones such as mobile cameras (e.g., in robotics or driving assistant systems). In this paper, we address the problem of self-learning detectors in an autonomous manner, i.e. (i) detectors continuously updating themselves to efficiently adapt to streaming data sources (contrary to transductive algorithms), (ii) without any labeled data strongly related to the target data stream (contrary to self-paced learning), and (iii) without manual intervention to set and update hyper-parameters. To that end, we propose an unsupervised, on-line, and self-tuning learning algorithm to optimize a multi-task learning convex objective. Our method uses confident but laconic oracles (high-precision but low-recall off-the-shelf generic detectors), and exploits the structure of the problem to jointly learn on-line an ensemble of instance-level trackers, from which we derive an adapted category-level object detector. Our approach is validated on real-world publicly available video object datasets.
Unsupervised domain adaptation approaches use annotated data from a fixed dataset (the source set) and only data from the target dataset. They often require one or more passes over a large pool of annotated samples in order to adapt the model to each new target dataset, which makes them impractical for continuous adaptation to streaming data. For instance, Taskar al @cite_16 leverage unseen'' features from instances classified with high confidence, learning which features are useful for classification, and Gong al @cite_21 reshape'' datasets to minimize their distribution mismatch. These methods assume that the target domain is stationary, with many unlabeled target samples readily available at each update (transductive setting @cite_6 ). Consequently, these approaches are not suited to non-stationary streaming scenarios, such as when using on-board cameras ( for autonomous driving or robotics).
@cite_16 @cite_21 @cite_6
2134125014 2157989183 2107008379
This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and therefore are sampled from related but different distributions. In particular, features may appear in some parts of the data that are rarely or never seen in others. In most situations with nonhomogeneous data, the training data is not representative of the distribution under which the classifier must operate. We propose a method, based on probabilistic graphical models, for utilizing unseen features during classification. Our method introduces, for each such unseen feature, a continuous hidden variable describing its influence on the class -- whether it tends to be associated with some label. We then use probabilistic inference over the test data to infer a distribution over the value of this hidden variable. Intuitively, we "learn" the role of this unseen feature from the test set, generalizing from those instances whose label we are fairly sure about. Our overall probabilistic model is learned from the training data. In particular, we also learn models for characterizing the role of unseen features; these models use "meta-features" of those features, such as words in the neighborhood of an unseen feature, to infer its role. We present results for this framework on the task of classifying news articles and web pages, showing significant improvements over models that do not use unseen features. In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting, pose, background, resolution, etc.) We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other to the maximum extent; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric formulation and efficient optimization procedure that can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks.