Pregled bibliografske jedinice broj: 857779
Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels
Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels // Pattern Recognition. 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings. Lecture Notes in Computer Science, Vol. 9796. / Rosenhahn, Bodo, Andres, Bjoern (ur.).
Hannover: Springer, 2016. str. 377-388 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 857779 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels
Autori
Krapac, Josip ; Šegvić, Siniša
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Pattern Recognition. 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings. Lecture Notes in Computer Science, Vol. 9796.
/ Rosenhahn, Bodo, Andres, Bjoern - Hannover : Springer, 2016, 377-388
ISBN
978-3-319-45885-4
Skup
38th German Conference on Pattern Recognition GCPR 2016.
Mjesto i datum
Hannover, Njemačka, 12.09.2016. - 15.09.2016
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Convolutional networks, weakly supervised localization
Sažetak
We address the problem of semantic segmentation of objects in weakly supervised setting, when only image-wide labels are available. We describe an image with a set of pre-trained convolutional features and embed this set into a Fisher vector. We apply the learned image classifier on the set of all image regions and propagate the region scores back to the pixels. Compared to the alternatives the proposed method is simple, fast in inference, and especially in training. The method displays very good performance of on two standard semantic segmentation benchmarks.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
