[
    {
        "id": "musleh-2026-gtu",
        "type_id": "journalpaper_notalk",
        "tu_id": null,
        "repositum_id": "20.500.12708/230849",
        "title": "Guiding through uncertainties in visual analytics: A survey",
        "date": "2026",
        "abstract": "Several uncertainties emerge in each component of the visual analytics (VA) cycle that hinder the user’s ability to make efficient and effective decisions, e.g., through missing data, model approximations, or visual mappings. Well-known VA strategies aim first at making users aware of these uncertainties, often through visual means. When visuals alone are insufficient to accurately quantify or communicate uncertainties, VA designers may rely on guidance to support users’ understanding of these uncertainties throughout the VA cycle. While prior VA research has attempted to conceptualize guidance, the ability and mechanisms for guidance to comprehensively address different sources of uncertainties remain an open question. In this survey, we characterize the relationships between uncertainties and guidance in VA literature. Our key contribution is a taxonomic framework that relates uncertainty sources to relevant guidance strategies and their respective profiles, i.e., roles, scopes, and features. Through this taxonomy, we discuss how guidance addresses uncertainties, identify research gaps, and promote a more comprehensive understanding of guidance strategies to support effective design for uncertainty in VA. Our survey underscores the effectiveness of guidance in navigating uncertainties with context-aware and multi-scope strategies. We highlight challenging opportunities for further research in this space, especially in the adjacent areas of accessibility and onboarding, and suggest new research areas, such as narrative and persuasion guidance to support uncertainty awareness in VA.",
        "authors_et_al": false,
        "substitute": null,
        "main_image": null,
        "sync_repositum_override": null,
        "repositum_presentation_id": null,
        "authors": [
            1867,
            5418,
            5628,
            5629,
            5624,
            966,
            1410
        ],
        "articleno": "104739",
        "doi": "10.1016/j.cag.2026.104739",
        "issn": "1873-7684",
        "journal": "Computers & Graphics",
        "pages": "25",
        "publisher": "PERGAMON-ELSEVIER SCIENCE LTD",
        "research_areas": [],
        "keywords": [
            "Guidance",
            "Uncertainty",
            "Visual Analytics",
            "Decision Support Systems"
        ],
        "weblinks": [],
        "files": [],
        "projects_workgroups": [
            "d9522"
        ],
        "url": "https://www.cg.tuwien.ac.at/research/publications/2026/musleh-2026-gtu/",
        "__class": "Publication"
    },
    {
        "id": "tekaya-2025-amo",
        "type_id": "inproceedings",
        "tu_id": null,
        "repositum_id": "20.500.12708/221607",
        "title": "A Matter of Time: Revealing the Structure of Time in Vision-Language Models",
        "date": "2025-10-27",
        "abstract": "Large-scale vision-language models (VLMs) such as CLIP have gained popularity for their generalizable and expressive multimodal representations. By leveraging large-scale training data with diverse textual metadata, VLMs acquire open-vocabulary capabilities, solving tasks beyond their training scope. This paper investigates the temporal awareness of VLMs, assessing their ability to position visual content in time. We introduce TIME10k, a benchmark dataset of over 10,000 images with temporal ground truth, and evaluate the time-awareness of 37 VLMs by a novel methodology. Our investigation reveals that temporal information is structured along a low-dimensional, non-linear manifold in the VLM embedding space. Based on this insight, we propose methods to derive an explicit ''timeline'' representation from the embedding space. These representations model time and its chronological progression and thereby facilitate temporal reasoning tasks. Our timeline approaches achieve competitive to superior accuracy compared to a prompt-based baseline while being computationally efficient. All code and data are available at https://tekayanidham.github.io/timeline-page/.",
        "authors_et_al": false,
        "substitute": null,
        "main_image": {
            "description": null,
            "filetitle": "teaser",
            "main_file": false,
            "use_in_gallery": true,
            "access": "public",
            "image_width": 1536,
            "image_height": 458,
            "name": "tekaya-2025-amo-teaser.png",
            "type": "image/png",
            "size": 4646052,
            "path": "Publication:tekaya-2025-amo",
            "url": "https://www.cg.tuwien.ac.at/research/publications/2025/tekaya-2025-amo/tekaya-2025-amo-teaser.png",
            "thumb_image_sizes": [
                16,
                64,
                100,
                175,
                300,
                600
            ],
            "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/tekaya-2025-amo/tekaya-2025-amo-teaser:thumb{{size}}.png"
        },
        "sync_repositum_override": "date,keywords,projects",
        "repositum_presentation_id": null,
        "authors": [
            5330,
            1110,
            5370
        ],
        "booktitle": "MM '25: Proceedings of the 33rd ACM International Conference on Multimedia",
        "date_from": "2025-10-27",
        "date_to": "2025-10-31",
        "doi": "10.1145/3746027.3758163",
        "event": "ACM International Conference on Multimedia 2025",
        "isbn": "979-8-4007-2035-2",
        "lecturer": [
            5330
        ],
        "location": "Dublin",
        "open_access": "yes",
        "pages": "10",
        "pages_from": "12371",
        "pages_to": "12380",
        "research_areas": [
            "InfoVis"
        ],
        "keywords": [
            "Multimodal representations",
            "Vision-language models",
            "Time modeling",
            "Time estimation",
            "Benchmark dataset"
        ],
        "weblinks": [
            {
                "href": "https://tekayanidham.github.io/timeline-page/",
                "caption": null,
                "description": null,
                "main_file": 1
            }
        ],
        "files": [
            {
                "description": null,
                "filetitle": "teaser",
                "main_file": false,
                "use_in_gallery": true,
                "access": "public",
                "image_width": 1536,
                "image_height": 458,
                "name": "tekaya-2025-amo-teaser.png",
                "type": "image/png",
                "size": 4646052,
                "path": "Publication:tekaya-2025-amo",
                "url": "https://www.cg.tuwien.ac.at/research/publications/2025/tekaya-2025-amo/tekaya-2025-amo-teaser.png",
                "thumb_image_sizes": [
                    16,
                    64,
                    100,
                    175,
                    300,
                    600
                ],
                "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/tekaya-2025-amo/tekaya-2025-amo-teaser:thumb{{size}}.png"
            }
        ],
        "projects_workgroups": [
            "d9522",
            "vis"
        ],
        "url": "https://www.cg.tuwien.ac.at/research/publications/2025/tekaya-2025-amo/",
        "__class": "Publication"
    },
    {
        "id": "aigner-2025-vhv",
        "type_id": "inproceedings",
        "tu_id": null,
        "repositum_id": "20.500.12708/225207",
        "title": "Visual Heritage: Visual Analytics and Computer Vision Meet Cultural Heritage (doc.funds.connect)",
        "date": "2025-05-07",
        "abstract": null,
        "authors_et_al": false,
        "substitute": null,
        "main_image": null,
        "sync_repositum_override": null,
        "repositum_presentation_id": null,
        "authors": [
            5540,
            966,
            5541,
            1511,
            5542,
            1110,
            5370
        ],
        "booktitle": "Abstractband: 18. Forschungsforum der österreichischen Fachhochschulen",
        "date_from": "2025-05-07",
        "date_to": "2025-05-08",
        "event": "18. Forschungsforum Der Österreichischen Fachhochschulen",
        "lecturer": [
            5543
        ],
        "location": "Wien",
        "pages": "2",
        "pages_from": "558",
        "pages_to": "559",
        "research_areas": [],
        "keywords": [
            "Artificial Intelligence",
            "Visual Analytics",
            "Computer Vision",
            "Cultural Heritage",
            "Visualization",
            "Computer Science"
        ],
        "weblinks": [],
        "files": [],
        "projects_workgroups": [
            "d9522"
        ],
        "url": "https://www.cg.tuwien.ac.at/research/publications/2025/aigner-2025-vhv/",
        "__class": "Publication"
    }
]