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{
    "pk": 28941,
    "title": "Robustness of Object Recognition under Extreme Occlusionin Humans and Computational Models",
    "subtitle": null,
    "abstract": "Most objects in the visual world are partially occluded, buthumans can recognize them without difficulty. However, it re-mains unknown whether object recognition models like convo-lutional neural networks (CNNs) can handle real-world occlu-sion. It is also a question whether efforts to make these modelsrobust to constant mask occlusion are effective for real-worldocclusion. We test both humans and the above-mentionedcomputational models in a challenging task of object recogni-tion under extreme occlusion, where target objects are heavilyoccluded by irrelevant real objects in real backgrounds. Ourresults show that human vision is very robust to extreme oc-clusion while CNNs are not, even with modifications to han-dle constant mask occlusion. This implies that the ability tohandle constant mask occlusion does not entail robustness toreal-world occlusion. As a comparison, we propose anothercomputational model that utilizes object parts/subparts in acompositional manner to build robustness to occlusion. Thisperforms significantly better than CNN-based models on ourtask with error patterns similar to humans. These findings sug-gest that testing under extreme occlusion can better reveal therobustness of visual recognition, and that the principle of com-position can encourage such robustness.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "visual recognition; occlusion; computationalmodel; neural network; psychophysics"
        }
    ],
    "section": "Papers with Poster Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/0d5666gw",
    "frozenauthors": [
        {
            "first_name": "Hongru",
            "middle_name": "",
            "last_name": "Zhu",
            "name_suffix": "",
            "institution": "The Johns Hopkins University, Baltimore",
            "department": ""
        },
        {
            "first_name": "Peng",
            "middle_name": "",
            "last_name": "Tang",
            "name_suffix": "",
            "institution": "The Johns Hopkins University, Baltimore",
            "department": ""
        },
        {
            "first_name": "Jeongho",
            "middle_name": "",
            "last_name": "Park",
            "name_suffix": "",
            "institution": "Harvard University",
            "department": ""
        },
        {
            "first_name": "Soojin",
            "middle_name": "",
            "last_name": "Park",
            "name_suffix": "",
            "institution": "Yonsei University",
            "department": ""
        },
        {
            "first_name": "Alan",
            "middle_name": "",
            "last_name": "Yuille",
            "name_suffix": "",
            "institution": "The Johns Hopkins University, Baltimore",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2019-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/28941/galley/18812/download/"
        }
    ]
}