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M-RRFS: A Memory-Based Robust Region Feature Synthesizer for Zero-Shot Object Detection

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Abstract

With the goal to detect both the object categories appearing in the training phase and those never have been observed before testing, zero-shot object detection (ZSD) becomes a challenging yet anticipated task in the community. Current approaches tackle this problem by drawing on the feature synthesis techniques used in the zero-shot image classification (ZSC) task without delving into the inherent problems of ZSD. In this paper, we analyze the out-standing challenges that ZSD presents compared with ZSC—severe intra-class variation, complex category co-occurrence, open test scenario, and reveal their interference to the region feature synthesis process. In view of this, we propose a novel memory-based robust region feature synthesizer (M-RRFS) for ZSD, which is equipped with the Intra-class Semantic Diverging (IntraSD), the Inter-class Structure Preserving (InterSP), and the Cross-Domain Contrast Enhancing (CrossCE) mechanisms to overcome the inadequate intra-class diversity, insufficient inter-class separability, and weak inter-domain contrast problems. Moreover, when designing the whole learning framework, we develop an asynchronous memory container (AMC) to explore the cross-domain relationship between the seen class domain and unseen class domain to reduce the overlap between the distributions of them. Based on AMC, a memory-assisted ZSD inference process is also proposed to further boost the prediction accuracy. To evaluate the proposed approach, comprehensive experiments on MS-COCO, PASCAL VOC, ILSVRC and DIOR datasets are conducted, and superior performances have been achieved. Notably, we achieve new state-of-the-art performances on MS-COCO dataset, i.e., 64.0\(\%\), 60.9\(\%\) and 55.5\(\%\) Recall@100 with IoU \(= 0.4, 0.5, 0.6 \) respectively, and 15.1\(\%\) mAp with IoU\(=0.5\), under the 48/17 category split setting. Meanwhile, experiments on the DIOR dataset actually build the earliest benchmark for evaluating zero-shot object detection performance on remote sensing images. https://github.com/HPL123/M-RRFS.

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Data Availability

The datasets generated during the current study are available in the MS COCO repository, https://cocodataset.org/#home, the PASCAL r- epository, http://host.robots.ox.ac.uk/pascal/VOC/, and the DIOR repository, https://pan.baidu.com/s/1iLKT0JQoKXEJTGNxt5lSMg#list/path=%2F.

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Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Peiliang Huang, Dingwen Zhang & Junwei Han

  2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    Peiliang Huang, Dingwen Zhang & Junwei Han

  3. School of Telecommunications Engineering, Xidian University, Xi’an, China

    De Cheng

  4. School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, China

    Longfei Han

  5. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Pengfei Zhu

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  1. Peiliang Huang
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  2. Dingwen Zhang
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  4. Longfei Han
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  6. Junwei Han
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Correspondence to Dingwen Zhang or Junwei Han.

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Communicated by Esa Rahtu.

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This work was supported in part by the Key-Area Research and Development Program of Guangdong Province (No. 2021B0101200001), the National Natural Science Foundation of China (Nos. U21B2048, 62322605, 62293543, 62202015), and the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center Project Grant (21KT008).

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Huang, P., Zhang, D., Cheng, D. et al. M-RRFS: A Memory-Based Robust Region Feature Synthesizer for Zero-Shot Object Detection. Int J Comput Vis 132, 4651–4672 (2024). https://doi.org/10.1007/s11263-024-02112-9

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