Visual Recognition by Request

• URL: http://arxiv.org/abs/2207.14227v1
• Date: Thu, 28 Jul 2022 16:55:11 GMT
• Title: Visual Recognition by Request
• Authors: Chufeng Tang, Lingxi Xie, Xiaopeng Zhang, Xiaolin Hu, Qi Tian
• Abstract summary: We present a novel protocol of annotation and evaluation for visual recognition. It does not require the labeler/algorithm to annotate/recognize all targets (objects, parts, etc.) at once, but instead raises a number of recognition instructions and the algorithm recognizes targets by request. We evaluate the recognition system on two mixed-annotated datasets, CPP and ADE20K, and demonstrate its promising ability of learning from partially labeled data.
• Score: 111.94887516317735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a novel protocol of annotation and evaluation for visual recognition. Different from traditional settings, the protocol does not require the labeler/algorithm to annotate/recognize all targets (objects, parts, etc.) at once, but instead raises a number of recognition instructions and the algorithm recognizes targets by request. This mechanism brings two beneficial properties to reduce the burden of annotation, namely, (i) variable granularity: different scenarios can have different levels of annotation, in particular, object parts can be labeled only in large and clear instances, (ii) being open-domain: new concepts can be added to the database in minimal costs. To deal with the proposed setting, we maintain a knowledge base and design a query-based visual recognition framework that constructs queries on-the-fly based on the requests. We evaluate the recognition system on two mixed-annotated datasets, CPP and ADE20K, and demonstrate its promising ability of learning from partially labeled data as well as adapting to new concepts with only text labels.

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