Abstract
To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario because they require that: (i) all target object classes are known beforehand, and (ii) a vast number of training examples is provided for each class. This evidence calls for novel methods to handle unknown object classes, for which fewer images are initially available (few-shot recognition). One way of tackling the problem is learning how to match novel objects to their most similar supporting example. Here, we compare different (shallow and deep) approaches to few-shot image matching on a novel data set, consisting of 2D views of common object types drawn from a combination of ShapeNet and Google. First, we assess if the similarity of objects learned from a combination of ShapeNet and Google can scale up to new object classes, i.e., categories unseen at training time. Furthermore, we show how normalising the learned embeddings can impact the generalisation abilities of the tested methods, in the context of two novel configurations: (i) where the weights of a Convolutional two-branch Network are imprinted and (ii) where the embeddings of a Convolutional Siamese Network are L2-normalised.
Highlights
As the fields of Artificial Intelligence (AI) and Robotics mature and evolve, an increasing number of hardware and software solutions have become available, reducing the costs and technical barriers of developing novel robotic platforms
We further extend this investigation to assess whether the Deep representations learned by similarity matching on ShapeNet can: (i) outperform the previously-explored shallow representations, as well as (ii) generalise to new object classes
The results obtained in [19], which are summarised in Table 3, albeit providing an improvement from random label assignment in all configurations, were not satisfactory to discriminate different object classes
Summary
As the fields of Artificial Intelligence (AI) and Robotics mature and evolve, an increasing number of hardware and software solutions have become available, reducing the costs and technical barriers of developing novel robotic platforms. The problem of real-time object recognition has reached satisfactory solutions [8,9,10,11] only in experimental scenarios where a very large amounts of human-annotated data is available and all object classes are assumed to be predetermined, known as the closed world assumption [20] Problems such as the paucity of training data or the adaptability to new learning environments are pervasive across all sub-fields of Artificial Intelligence (AI), and specific to the object recognition area.
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