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ZF-SSE: A Unified Sequential Semantic Encoder for Zero-Few-Shot Learning | IEEE Conference Publication | IEEE Xplore

ZF-SSE: A Unified Sequential Semantic Encoder for Zero-Few-Shot Learning


Abstract:

Humans can inherently recognize various objects and gestures either from very few examples or from their descriptions. However, supervised gesture classification methods ...Show More

Abstract:

Humans can inherently recognize various objects and gestures either from very few examples or from their descriptions. However, supervised gesture classification methods require hundreds of examples to learn to classify, and cannot rapidly generalize from either few examples or from their high-level descriptions. This gap can be bridged using few-shot (FSL) and zero-shot (ZSL) learning methods (jointly referred to as ZFSL). Previous approaches studied the problems of ZSL and FSL in isolation, and there are few works concerned with ZFSL for temporal problems such as unfamiliar gesture recognition. In this context, this paper represents categories as semantic descriptions in a high-level attribute space and proposes a unified framework to facilitate ZFSL. This work further introduces an approach referred to as Unified Sequential Semantic Encoder (ZF -SSE) to explore temporal patterns and to predict the semantic information through simultaneously optimizing for both classification and semantic tasks. The proposed framework is validated in the domain of hand gesture recognition and the results show that the ZF -SSE approach significantly outperforms existing approaches by at least 4-10% in zero-shot, few-shot, and open-set experimental conditions.
Date of Conference: 15-18 December 2021
Date Added to IEEE Xplore: 12 January 2022
ISBN Information:
Conference Location: Jodhpur, India

Funding Agency:


References

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