TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Cross-granularity graph inference for semantic video object segmentation

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Sivut4544-4550
Sivumäärä7
ISBN (elektroninen)9780999241103
TilaJulkaistu - 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaINTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)1045-0823

Conference

ConferenceINTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
Ajanjakso1/01/00 → …

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.

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