Data

EmbSCU

Edith Cowan University
Bodo Rosenhahn (Aggregated by) David Suter (Aggregated by) Jumana Abu-Khalaf (Aggregated by) Mariia Khan (Aggregated by) Yue Qiu (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25958/e7jp-p396&rft.title=EmbSCU&rft.identifier=10.25958/e7jp-p396&rft.publisher=Edith Cowan University&rft.description=This dataset was created for the evaluation of the EmbSCU method, suitable for solving the Scene Change Understanding (SCU) task. The SCU task involves predicting a changed location, describing a change, and generating language instructions for the robotic agent to revert a change. Current datasets, related to scene change understanding, can be divided into scene change detection (SCD) and image difference captioning (IDC) datasets. Unlike existing approaches, EmbSCU facilitates simultaneous change detection, description and language-based rearrangement instruction generation for the agent to revert changes. Although the EmbSCU dataset is simulated, it is highly complex, incorporating 104 unique indoor Ai2Thor rooms. EmbSCU encompasses 54 interactable indoor object categories.&rft.creator=Bodo Rosenhahn&rft.creator=David Suter&rft.creator=Jumana Abu-Khalaf&rft.creator=Mariia Khan&rft.creator=Yue Qiu&rft.creator=Yuren Cong&rft.date=2026&rft.relation=https://doi.org/10.1109/IROS58592.2024.10801354&rft_rights= http://creativecommons.org/licenses/by-nc-sa/4.0/&rft_subject=scene change detection&rft_subject=change description&rft_subject=scene change understanding&rft_subject=Computer Sciences&rft_subject=Data Science&rft.type=dataset&rft.language=English Access the data
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Full description

This dataset was created for the evaluation of the EmbSCU method, suitable for solving the Scene Change Understanding (SCU) task. The SCU task involves predicting a changed location, describing a change, and generating language instructions for the robotic agent to revert a change. Current datasets, related to scene change understanding, can be divided into scene change detection (SCD) and image difference captioning (IDC) datasets. Unlike existing approaches, EmbSCU facilitates simultaneous change detection, description and language-based rearrangement instruction generation for the agent to revert changes. Although the EmbSCU dataset is simulated, it is highly complex, incorporating 104 unique indoor Ai2Thor rooms. EmbSCU encompasses 54 interactable indoor object categories.

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Subjects
Computer Sciences | Data Science | change description | scene change detection | scene change understanding |

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Identifiers
ACN 633 798 857