# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 1 Introduction  
Demands to expand the scale of categories available for object recognition have been aroused by a rapid increase in the sizes and types of image data and the recent success of large-scale recognition systems [1]. However, manually constructing additional annotations and retraining existing image classifiers through supervised learning is impractical and costly, which limits the scalability of existing systems. To alleviate that limitation, a variant of transfer learning, zero-shot learning (ZSL) has drawn the attention of the computer vision community [2; 3; 4; 5]. ZSL is inspired by the human capacity to recognize objects without any visual samples using background knowledge already read or heard. The key is to transfer semantic knowledge about familiar (seen) objects to imagine unfamiliar (unseen) objects. For instance, a human can easily recognize an unseen zebra based on visual experiences with a horse and a watermelon, if it is known that a zebra looks like a horse with stripes on its body.In the same way, the objective of ZSL methods is to increase the cognitive capability of a visual classifier by using annotated training sets of seen class labels and external knowledge about the semantic relations between seen and unseen categories to allow the classifier to infer the class labels of novel objects. In this context, external knowledge is generally represented as non-visually using attributes [6; 7], semantic embedding [8], and knowledge graphs [9; 10]. To transfer knowledge, zero-shot recognition (ZSR) assumes that visually similar objects tend to also be semantically similar, which implies that the vector representations of their class labels are close. Most existing ZSL methods thus focus on learning to recognize inherent visual features (e.g., color, shape, and texture) and providing a map between the visual and semantic representations. However, the correlation between visual and semantic information is not always assured. For example, the unseen object, monitor, may be confused with a frame or a window because it has a square, edged shape with inside contents. Thus, existing ZSR models have critical limitations because they rely on visual information about a single unseen object. In contrast, when people try to identify an unseen object, they naturally refer to the circumstances surrounding the object, such as other objects and their relative positions, as well as the visual characteristics of the target object. In addition, it is common to infer more from relatively close objects than from more distant objects (e.g., the toothpaste beside a toothbrush and not the mirror in the scene). When modeling a zero-shot classifier, the correct label monitor could be inferred as appropriate when the surroundings suggest an office environment through the appearance of seen objects such as a keyboard and a notebook. That is, the classifier should use surrounding information to determine the type of object appropriate in a given environment. In this paper, we use those intuitions to propose a novel ZSR method that leverages context based on similarity measurements and distance-weighting between a target unseen object and surrounding objects. We aim to enhance the performance of existing instance-level ZSR that relies only on individual visual information about the target. To identify the context, our method uses cognitive information about the surrounding objects and obtains their similarity information for the target object using three different measures on a knowledge graph, a semantic embedding space, and both together. Moreover, distance weighting is applied to each piece of similarity information to focus on nearby surrounding objects by defining a distance calculation formula. To evaluate the effectiveness of our method, we adopted several existing ZSL models as baselines and performed extensive experimental evaluations on two different datasets containing small and large amounts of target categories. The main contributions of this work are as follows.