In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers.
Salient object detection in complex scenes and environments is a challenging research topic.
RGBT salient object detection (SOD) aims to segment the common prominent regions of visible and thermal infrared images.
Then, we design a novel dual-decoder to conduct the interactions of multi-level features, two modalities and global contexts.
Classifying the confusing samples in the course of RGBT tracking is a quite challenging problem, which hasn't got satisfied solution.
To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge guided feature learning for accurate salient object detection.
In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object.
In this paper, we propose an effective approach for RGB-T image saliency detection.