Temporal Tessellation: A Unified Approach for Video Analysis

Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים


We present a general approach to video understanding, inspired by semantic transfer techniques that have been successfully used for 2D image analysis. Our method considers a video to be a 1D sequence of clips, each one associated with its own semantics. The nature of these semantics - natural language captions or other labels - depends on the task at hand. A test video is processed by forming correspondences between its clips and the clips of reference videos with known semantics, following which, reference semantics can be transferred to the test video. We describe two matching methods, both designed to ensure that (a) reference clips appear similar to test clips and (b), taken together, the semantics of the selected reference clips is consistent and maintains temporal coherence. We use our method for video captioning on the LSMDC'16 benchmark, video summarization on the SumMe and TV-Sum benchmarks, Temporal Action Detection on the Thumos2014 benchmark, and sound prediction on the Greatest Hits benchmark. Our method not only surpasses the state of the art, in four out of five benchmarks, but importantly, it is the only single method we know of that was successfully applied to such a diverse range of tasks.

שפה מקוריתאנגלית
כותר פרסום המארחProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
מוציא לאורInstitute of Electrical and Electronics Engineers Inc.
מספר עמודים11
מסת"ב (אלקטרוני)9781538610329
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 22 דצמ׳ 2017
אירוע16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, איטליה
משך הזמן: 22 אוק׳ 201729 אוק׳ 2017

סדרות פרסומים

שםProceedings of the IEEE International Conference on Computer Vision
ISSN (מודפס)1550-5499


כנס16th IEEE International Conference on Computer Vision, ICCV 2017

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