Forest fire is becoming one of the most significant natural disasters at the expense of ecology and economy. In this article, we develop an effective SqueezeNet based asymmetric encoder-decoder U-shape architecture, Attention U-Net and SqueezeNet (ATT Squeeze U-Net), mainly functions as an extractor and a discriminator of forest fire. This model takes attention mechanism to highlight useful features and suppress irrelevant contents by embedding Attention Gate (AG) units in the skip connection of U-shape structure. In this way, salient features are emphasized so that the proposed method could be competent at forest fire segmentation tasks with a small number of parameters. Specifically, we first replace classical convolution layer by a depthwise one and engage a Channel Shuffle operation as a feature communicator in the Fire module of classical SqueezeNet. Then, this modified SqueezeNet is employed as a substitution of the encoder of Attention U-Net and a corresponding DeFire module designed is combined into the decoder as well. Finally, to classify true fire, we take use of a fragment of the encoder in ATT Squeeze U-Net. The experimental results of modified SqueezeNet integrated Attention U-Net show that a competitive accuracy at 0.93 and an average prediction time at 0.89 second per image are achieved for reliable real-time forest fire detection.

In recent years, learning-based single image dehazing networks have been comprehensively developed. However, performance improvement is limited due to domain shift between trained synthetic hazy images and untrained real-world hazy images. To alleviate this issue, this paper proposes a real-world dehazing targeted training scheme which nearly realizes paired real-world data training. As a result, a Twofold Multi-scale Generative Adversarial Network (TMS-GAN) consisting of a Haze-generation GAN (HgGAN) and a Haze-removal GAN (HrGAN) is designed. HgGAN attributes real haze properties to synthetic images and HrGAN removes haze from both synthetic and generated fake realistic data under supervision. Thus, the proposed method can better adapt to real-world image dehazing using this cooperative training scheme. Meanwhile, several structural advances of TMS-GAN also improve dehazing performance. Specifically, a haze residual map based on atmospheric scattering model is deduced in HgGAN for fake realistic data generation. The dual-branch generator in HrGAN draws attention to detail restoration by one branch along with another color-branch. A plug-and-play Multi-attention Progressive Fusion Module (MAPFM) is proposed and inserted in both HgGAN and HrGAN. MAPFM incorporates multi-attention mechanism to guide multi-scale feature fusion in a progressive manner, in which Adjacency-attention Block (AAB) can capture contributing features of each level and Self-attention Block (SAB) can establish non-local dependency of feature fusion. Experiments on mainstream benchmarks show that the proposed framework is superior especially on real-world hazy images among single image dehazing methods.

Few-shot semantic segmentation is a challenging task that aims to segment novel classes in the query images given only a few annotated support samples. Most existing prototype-based approaches extract global or local prototypes by global average pooling (GAP) or clustering to represent all object information. Subsequently, the prototype information is employed as guidance for query image segmentation. However, these frameworks fail to fully mine the object details and ignore information from query images. Consequently, we propose a Dual-Guided Frequency Prototype Network (DGFPNet) to solve these issues. Specifically, to mine the global and local object information, a Frequency Prototype Generation Module (FPGM) is first proposed to extract more comprehensive frequency prototypes by multi-frequency pooling (MFP) in the DCT domain. Then, with the guidance of support and query information, a Dual-Guided Selection Module (DGSM) is presented to produce the query attention mask and select more effective prototypes. Based on the query attention mask and support information, the generalized object information is integrated into the feature with the proposed Feature Generalization Module (FGM). Finally, we propose a Multi-Dimension Feature Enrichment Decoder Module (MDFEDM) to capture multi-dimension object information and tackle hard pixels for refining the final segmentation results. Extensive experiments on PASCAL-5 ⁱ and COCO-20 ⁱ show that our model achieves new state-of-the-art performances. Our code will be released at https://github.com/ChunLinWen/DGFPNet.

Text-to-image synthesis task aims at generating images consistent with input text descriptions and is well developed by the Generative Adversarial Network (GAN). Although GAN based image generation approaches have achieved promising results, synthesizing quality is sometimes unsatisfied due to discursive generation of background and object. In this article, we propose a cooperative up-sampling based Dual Generator attentional GAN (DGattGAN) to generate high-quality images from text description. To achieve this, two generators with individual generation purpose are established to decouple object and background generation. In particular, we introduce a cooperative up-sampling mechanism to build cooperation between object and background generators during training. This strategy is potentially very useful as any dual generator architecture in GAN models can benefit from this mechanism. Furthermore, we propose an asymmetric information feeding scheme to distinguish two synthesis tasks, such that each generator only synthesizes based on semantic information they accept. Taking advantage of effective dual generator, the attention mechanism we incorporated on object generator could devote to fine-grained details generation on actual targeted objects. Experiments on Caltech-UCSD Bird (CUB) and Oxford-102 datasets suggest that generated images by the proposed model are more realistic and consistent with input text, and DGattGAN is competent compared to state-of-the-art methods according to Inception Score (IS) and R-precision metrics. Our codes are available at: https://github.com/ecfish/DGattGAN .