Name
Title | ||
|---|---|---|
Group-level Cohesion Prediction using Deep Learning Models with A Multi-stream Hybrid Network. |
Abstract | ||
|---|---|---|
In this paper, we propose a hybrid deep learning network for predicting group cohesion in images. It is a kind of regression problem and its objective is to predict the Group Cohesion Score (GCS), which is in the range of [0,3]. In order to solve this issue, we exploit four types of visual cues, such as scene, skeleton, UV coordinates and face image, along with state-of-the-art convolutional neural networks (CNNs). We use not only fusion but also ensemble methods to combine these approaches. Our proposed hybrid network achieves 0.517 and 0.416 mean square errors (MSEs) on validation and testing sets, respectively. We finally achieved the first place on the Group-level Cohesion Sub-challenge (GC) in the EmotiW 2019.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3340555.3355715 | ICMI |
Keywords | Field | DocType |
EmotiW2019, Group-level Cohesion Prediction, Multi-modal, Scene Understanding | Cohesion (chemistry),Computer science,Human–computer interaction,Artificial intelligence,Deep learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6860-5 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tien Xuan Dang | 1 | 1 | 0.35 |
| Soo-Hyung Kim | 2 | 191 | 49.03 |
| Hyungjeong Yang | 3 | 455 | 47.05 |
| Gueesang Lee | 4 | 208 | 52.71 |
| Thanh-Hung Vo | 5 | 1 | 0.35 |