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The goal of Camouflaged object detection (COD) is to detect objects that are visually embedded in their surroundings. Existing COD methods only focus on detecting camouflaged objects from seen classes, while they suffer from performance degradation to detect unseen classes. However, in a real-world scenario, collecting sufficient data for seen classes is extremely difficult and labeling them requires high professional skills, thereby making these COD methods not applicable. In this paper, we propose a new zero-shot COD framework (termed as ZSCOD), which can effectively detect the never unseen ...

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Zero-shot cross-modal retrieval (ZS-CMR) performs the task of cross-modal retrieval where the classes of test categories have a different scope than the training categories. It borrows the intuition from zero-shot learning which targets to transfer the knowledge inferred during the training phase for seen classes to the testing phase for unseen classes. It mimics the real-world scenario where new object categories are continuously populating the multi-media data corpus. Unlike existing ZS-CMR approaches which use generative adversarial networks (GANs) to generate more data, we propose Inter-Mo...