Cross-granularity graph inference for semantic video object segmentation
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
| Sivut | 4544-4550 |
| Sivumäärä | 7 |
| ISBN (elektroninen) | 9780999241103 |
| Tila | Julkaistu - 2017 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE - Kesto: 1 tammikuuta 1900 → … |
Julkaisusarja
| Nimi | |
|---|---|
| ISSN (elektroninen) | 1045-0823 |
Conference
| Conference | INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
We address semantic video object segmentation via a novel cross-granularity hierarchical graphical model to integrate tracklet and object proposal reasoning with superpixel labeling. Tracklet characterizes varying spatial-temporal relations of video object which, however, quite often suffers from sporadic local outliers. In order to acquire highquality tracklets, we propose a transductive inference model which is capable of calibrating shortrange noisy object tracklets with respect to longrange dependencies and high-level context cues. In the center of this work lies a new paradigm of semantic video object segmentation beyond modeling appearance and motion of objects locally, where the semantic label is inferred by jointly exploiting multi-scale contextual information and spatialtemporal relations of video object. We evaluate our method on two popular semantic video object segmentation benchmarks and demonstrate that it advances the state-of-the-art by achieving superior accuracy performance than other leading methods.