Class Agnostic Instance-level Descriptor For Visual Instance Search

Abstract

Despite the great success of the deep features in content-based image retrieval, the visual instance search remains challenging due to the lack of effective instance-level feature representation. Supervised or weakly supervised object detection methods are not the appropriate solutions due to their poor performance on the unknown object categories. In this paper, based on the feature set output from self-supervised ViT, the instance-level region discovery is modeled as detecting the compact feature subsets in a hierarchical fashion. The hierarchical decomposition results in a hierarchy of instance regions. On the one hand, this kind of hierarchical decomposition well addresses the problem of object embedding and occlusions, which are widely observed in real scenarios. On the other hand, the non-leaf nodes and leaf nodes on the hierarchy correspond to the instance regions in different granularities within an image. Therefore, features in uniform length are produced for these instance re

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