Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel task named Instance of Interest Detection (IOID) to provide instance-level user interest modeling for image semantic description. IOID focuses on extracting the instances which are beneficial to represent image content, while other related tasks such as saliency analysis, attention model and instance segmentation extract the regions attracting visual attention or with a predefined category. To this end, we propose a Cross-influential Network for IOID, which integrates both visual saliency and semantic context. Moreover, we contribute the first dataset IOID evaluation, which consists of 45,000 images from MSCOCO with manually annotated instances of interest. Our method outperforms the state-of-the-art baselines on this dataset.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3343031.3350931 | Proceedings of the 27th ACM International Conference on Multimedia |
Keywords | Field | DocType |
instance extraction, instance of interest, instance of interest annotation, instance of interest detection, interest estimation | Computer science,Multimedia | Conference |
ISBN | Citations | PageRank |
978-1-4503-6889-6 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Yu | 1 | 0 | 2.03 |
| Haonan Wang | 2 | 0 | 1.35 |
| Tongwei Ren | 3 | 328 | 30.22 |
| Jinhui Tang | 4 | 5180 | 212.18 |
| Gangshan Wu | 5 | 275 | 36.63 |