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One-shot learning is a challenging problem where the aim is to recognize a class identified by a single training image. Given the practical importance of one-shot learning, it seems surprising that the rich information present in the class tag itself has largely been ignored. Most existing approaches restrict the use of the class tag to finding similar classes and transferring classifiers or metrics...
The problem of fine-grained object recognition is very challenging due to the subtle visual differences between different object categories. In this paper, we propose a task-driven progressive part localization (TPPL) approach for fine-grained object recognition. Most existing methods follow a two-step approach that first detects salient object parts to suppress the interference from background scenes...
High-level visual recognition such as scene classification is a challenging task in computer vision. In this paper, we propose an image descriptor based on semantic cliques obtained by high-order pure dependence, and the image is represented by a vector whose element denotes the probability of containing each object cliques. Compared with using single objects as attributes, such representation carries...
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