Name
Abstract | ||
|---|---|---|
In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG). To detect co-saliency (segmentation) accurately, the core problem is to well model inter-image relations between an image group. Some methods design sophisticated modules, such as recurrent neural network (RNN), to address this problem. However, order-sensitive problem is the major drawback of RNN, which heavily affects the stability of proposed CoSOD (CoSEG) model. In this paper, inspired by RNN-based model, we first propose a multi-path stable recurrent unit (MSRU), containing dummy orders mechanisms (DOM) and recurrent unit (RU). Our proposed MSRU not only helps CoSOD (CoSEG) model captures robust inter-image relations, but also reduces order-sensitivity, resulting in a more stable inference and training process. Moreover, we design a cross-order contrastive loss (COCL) that can further address order-sensitive problem by pulling close the feature embedding generated from different input orders. We validate our model on five widely used CoSOD datasets (CoCA, CoSOD3k, Cosal2015, iCoseg and MSRC), and three widely used datasets (Internet, iCoseg and PASCAL-VOC) for object co-segmentation, the performance demonstrates the superiority of the proposed approach as compared to the state-of-the-art (SOTA) methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIP.2022.3212906 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
Keywords | DocType | Volume |
Task analysis, Image segmentation, Saliency detection, Training, Recurrent neural networks, Feature extraction, Semantics, Co-saliency detection, object co-segmentation, recurrent neural network, contrastive loss | Journal | 31 |
ISSN | Citations | PageRank |
1057-7149 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Li | 1 | 8 | 8.65 |
| Lv Tang | 2 | 0 | 0.34 |
| Senyun Kuang | 3 | 0 | 1.01 |
| Mofei Song | 4 | 0 | 0.34 |
| Shouhong Ding | 5 | 0 | 1.69 |