The purpose of saliency detection is to detect significant regions in the image. Great progress on salient object detection has been made using from deep-learning frameworks. How to effectively extract and integrate multiscale information with different depths is an open problem for salient object detection. In this paper, we propose a processing mechanism based on a balanced attention module and interactive residual module. The mechanism addressed the acquisition of the multiscale features by capturing shallow and deep context information. For effective information fusion, a modified bi-directional propagation strategy was adopted. Finally, we used the fused multiscale information to predict saliency features, which were combined to generate the final saliency maps. The experimental results on five benchmark datasets show that the method is on a par with the state of the art for image saliency datasets, especially on the PASCAL-S datasets, where the MAE reaches 0.092, and on the DUT-OMROM datasets, where the F-measure reaches 0.763.

This paper presents a method based on Convolutional Neural Networks(CNN) and manually-selected feature fusion to improve the accuracy for person re-identification. Firstly, features of input images are extracted based on Inception structure. Secondly, a manually-selected feature module is added to adjust the learning of the Inception network for obtaining fusion features which are used for a softmax classifier. Experimental results indicate that compared with the networks without Inception structure, the proposed network on the VIPeR dataset improves the Rank-1 accuracy by 3.97% on LFDA.

The highlighted area or object is defined as the salient region or salient object. For salient object detection, the main challenges are still the clarity of the boundary information of the salient object and the positioning accuracy of the salient object in the complex background, such as noise and occlusion. To remedy these issues, it is proposed that the asynchronous cascade saliency detection algorithm based on a deep network, which is embedded in an encoder–decoder architecture. Moreover, the lightweight hybrid attention module is designed to obtain the explicit boundaries of salient regions. In order to effectively improve location information of salient objects, this paper adopts a bi-directional asynchronous cascade fusion strategy, which generates prediction maps with higher accuracy. The experimental results on five benchmark datasets show that the proposed network HACNet is on a par with the state of the art for image saliency datasets.

This paper presents PAF-Net, an image dehazing framework that integrates physical priors and adaptive feature modeling. Its aims to enhance image restoration quality in complex non-uniform haze scenes, while maintaining moderate model complexity and computational cost. Built upon the classical FFA-Net backbone, PAF-Net enhances dehazing performance from three complementary perspectives: spatial attention decision, physics-consistent feature representation in feature space, and operator-level adaptability. These perspectives are realized by three corresponding modules. Specifically, a Fog-aware Concentration Attention (FCA) module is introduced. It embeds haze-related spatial guidance into attention-weight generation, reducing attention–allocation imbalance under spatially non-uniform haze and strengthening the model’s focus on dense-haze regions. To improve physical feature consistency under non-uniform haze, a Physics-aware Feature Dehazing Unit (PFDU) is further designed to explicitly model and reorganize transmission-related and atmospheric-light-related feature components under atmospheric scattering priors. In addition, a Dynamic Convolution Module (DCM) is incorporated to adapt convolutional responses at the sample level according to global degradation patterns, enhancing robustness across diverse haze conditions. Experiments on RESIDE SOTS and real-world benchmarks (Dense-Haze and NH-Haze) demonstrate that PAF-Net achieves higher PSNR/SSIM and yields more natural visual results than representative methods. Further experiments and evaluations based on paired remote sensing datasets have verified the applicability of the designed PAF-Net algorithm in remote sensing scenes with spatially varying haze, as well as the algorithm’s generalization ability.