Salient object detection in optical remote sensing images (ORSI-SOD) aims to segment salient regions from high-resolution remote sensing images. However, most existing ORSI-SOD methods primarily rely on feature learning of regions to address the issue of blurred edges, while neglecting the potential advantages of similarity calculation in inferring edge clues. To overcome this issue, we propose a novel network with similarity perception termed edge-guided refinement network (ERNet), which distinguishes the edge region of salient objects from coarse to fine through two-stage similarity calculation. Firstly, we introduce the adaptive uncertainty calibration module (AUCM), which utilizes the proposed similarity-based edge perception mechanism (SEPM) to adaptively calibrate edge uncertain information. Secondly, to effectively capture global semantic information, we propose the hierarchical semantic reconstruction module (HSRM), which comprehensively correlates different levels of semantic clues from both internal and external perspectives. Finally, to supplement the local detail of salient objects, we design the dynamic detail interaction module (DDIM), which dynamically extracts detail information of objects at different scales. Extensive experiments on three challenging benchmark datasets have demonstrated the remarkable superiority of our ERNet compared to 30 state-of-the-art models. The source codes will be publicly available at https://github.com/xinwang11/ERNet.

Visual question answering (VQA) is a difficult multimodal task that requires answering questions about images. It requires a fine-grained level of understanding of both the visual content of the image and the textual content of the question. However, most of the existing models perform weakly in filtering noisy information and are unable to fuse features from multiple modalities effectively. To resolve the above restriction, we propose a novel multimodal gate fusion network (MGFN), which consists of an attention-on-attention interaction module (AoAIM) and a multimodal gate fusion module (MGFM). The role of AoAIM is to capture intra-modal and inter-modal dependencies and to filter out some irrelevant attention. The proposed MGFM can effectively fuse textual and visual features based on the relative importance of textual and visual modalities. We have performed many ablation experiments on the VQA-v2 dataset to validate the effectiveness of AoAIM and MGFM. The ablation experiments demonstrate that both AoAIM and MGFM play a key role in improving the performance of the model. By embedding these two modules, MGFN performs better than the previous state-of-the-art (SOTA) model on the VQA-v2 dataset. Particularly, the MGFN achieves an overall accuracy of 71.68% on the test-dev set and 72.12% on the test-std set.

Effective fusion of different types of features is the key to salient object detection (SOD). The majority of the existing network structure designs are based on the subjective experience of scholars, and the process of feature fusion does not consider the relationship between the fused features and the highest-level features. In this paper, we focus on the feature relationship and propose a novel global attention unit, which we term the “perception-and-regulation” (PR) block, that adaptively regulates the feature fusion process by explicitly modelling the interdependencies between features. The perception part uses the structure of the fully connected layers in the classification networks to learn the size and shape of the objects. The regulation part selectively strengthens and weakens the features to be fused. An imitating eye observation module (IEO) is further employed to improve the global perception capabilities of the network. The imitation of foveal vision and peripheral vision enables the IEO to scrutinize highly detailed objects and to organize a broad spatial scene to better segment objects. Sufficient experiments conducted on the SOD datasets demonstrate that the proposed method performs favourably against the 29 state-of-the-art methods.

The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the information validity of the two modes and balance their influence. Our framework is divided into three phases: perception phase, recoding mixing phase, and integration phase. Specifically, a perception encoder is adopted to extract multi-level single-modal features, which lays the foundation for multi-modal semantic comparative analysis. Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder. The recoding mixer is responsible for recoding and mixing the balanced multi-modal information. Finally, the hybrid branch decoder completes the multi-level feature integration under the guidance of an optional edge guidance stream (OEGS). Experiments on 8 popular benchmarks verify that our framework has better overall performance than the other 28 state-of-the-art algorithms.

Bounding box regression is a very effective method to improve the localization accuracy of object detection. Recently, the IoU-based regression losses have been widely used in object detection algorithms. However, we observe that they degenerate seriously in the late training period, leading to slow convergence and inaccurate localization. In this paper, we design a Fast Intersection over Union (FIoU) loss, which can not only keep the advantages but also solve the weakness of IoU-based losses. Furthermore, FIoU can be directly applied to Non-Maximum Suppression (NMS) as a criterion to improve the localization performance. Numerous experiments on two popular benchmark datasets show that our method is superior to other the-state-of-art methods.