Multimodal salient object detection via adversarial learning with collaborative generator

Multimodal salient object detection(MSOD), which utilizes multimodal information (e.g., RGB image and thermal infrared or depth image) to detect common salient objects, has received much attention recently. Different modalities reflect different appearance properties of salient objects, some of which could contribute to improving the precision and/or recall of MSOD. To greatly improve both Precision and Recall by fully exploring multimodal data, in this work, we propose an effective adversarial learning framework based on a novel collaborative generator for accurate multimodal salient object detection. In particular, the collaborative generator consists of three generators (generator1, generator2 and generator3), which aim at decreasing the false positive and false negative of the generated saliency maps and improving F-measure of the final saliency maps respectively. Generator1 and generator2 contain two encoder–decoder networks for multimodal inputs, and we propose a new co-attention model to perform adaptive interactions between different modalities. Furthermore, we apply generator3 to integrate feature maps from generator1 and generator2 in a complementary way. Through adversarially learning the collaborative generator and discriminator, both Precision and Recall of the predicted maps are boosted with the complementary benefits of multimodal data. Extensive experiments on three RGBT datasets and six RGBD datasets show that our method performs quite well against state-of-the-art MSOD methods.