On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey

Jingcai Guo; Zhijie Rao; Zhi Chen; Jingren Zhou; Dacheng Tao; Song Guo

Abstract

Zero-shot image recognition (ZSIR) aims to recognize and reason in unseen domains by learning generalized knowledge from limited data in the seen domain. In resource-constrained scenarios such as edge intelligence, zero-shot learning can significantly reduce data requirements, thereby alleviating the burden of data annotation, storage and communication. The gist of ZSIR is constructing a well-aligned mapping between the input visual space and the target semantic space, which is a bottom-up paradigm inspired by the process by which humans observe the world. In recent years, ZSIR has witnessed significant progress on a broad spectrum, from theory to algorithm design, as well as widespread applications. However, to the best of our knowledge, there remains a lack of a systematic review of ZSIR from an element-wise perspective, i.e., learning fine-grained elements of data and their inferential associations. As the element-wise paradigm shows great potential for adapting to dynamic edge environments with limited memory and low latency, this paper thoroughly investigates recent advances in element-wise ZSIR and provides a sound basis for its future development. Concretely, we first integrate three basic ZSIR tasks, i.e., object recognition, compositional recognition, and foundation model-based open-world recognition, into a unified element-wise paradigm and provide a detailed taxonomy and analysis of the main approaches. Next, we summarize the benchmarks, covering technical implementations, standardized datasets, and some more details as a library. Last, we sketch out related applications, discuss vital challenges, and suggest potential future directions.

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