Recently, deep learning methods based on three-dimensional (3-D) convolution have been widely used in the hyperspectral image (HSI) classification tasks and shown good classification performance. However, affected by the irregular distribution of various classes in HSI datasets, most previous 3-D convolutional neural network (CNN)-based models require more training samples to obtain better classification accuracies. In addition, as the network deepens, which leads to the spatial resolution of feature maps gradually decreasing, much useful information may be lost during the training process. Therefore, how to ensure efficient network training is key to the HSI classification tasks. To address the issue mentioned above, in this paper, we proposed a 3-DCNN-based residual group channel and space attention network (RGCSA) for HSI classification. Firstly, the proposed bottom-up top-down attention structure with the residual connection can improve network training efficiency by optimizing channel-wise and spatial-wise features throughout the whole training process. Secondly, the proposed residual group channel-wise attention module can reduce the possibility of losing useful information, and the novel spatial-wise attention module can extract context information to strengthen the spatial features. Furthermore, our proposed RGCSA network only needs few training samples to achieve higher classification accuracies than previous 3-D-CNN-based networks. The experimental results on three commonly used HSI datasets demonstrate the superiority of our proposed network based on the attention mechanism and the effectiveness of the proposed channel-wise and spatial-wise attention modules for HSI classification. The code and configurations are released at Github.com.

In recent years, deep learning methods have been widely used in the hyperspectral image (HSI) classification tasks. Among them, spectral-spatial combined methods based on the three-dimensional (3-D) convolution have shown good performance. However, because of the three-dimensional convolution, increasing network depth will result in a dramatic rise in the number of parameters. In addition, the previous methods do not make full use of spectral information. They mostly use the data after dimensionality reduction directly as the input of networks, which result in poor classification ability in some categories with small numbers of samples. To address the above two issues, in this paper, we designed an end-to-end 3D-ResNeXt network which adopts feature fusion and label smoothing strategy further. On the one hand, the residual connections and split-transform-merge strategy can alleviate the declining-accuracy phenomenon and decrease the number of parameters. We can adjust the hyperparameter cardinality instead of the network depth to extract more discriminative features of HSIs and improve the classification accuracy. On the other hand, in order to improve the classification accuracies of classes with small numbers of samples, we enrich the input of the 3D-ResNeXt spectral-spatial feature learning network by additional spectral feature learning, and finally use a loss function modified by label smoothing strategy to solve the imbalance of classes. The experimental results on three popular HSI datasets demonstrate the superiority of our proposed network and an effective improvement in the accuracies especially for the classes with small numbers of training samples.

With the rapid development of social media, visual sentiment analysis from image or video has become a hot spot in visual understanding researches. In this work, we propose an effective approach using visual and textual fusion for sentiment analysis of short GIF videos with textual descriptions. We extract both sequence-level and frame-level visual features for each given GIF video. Next, we build a visual sentiment classifier by using the extracted features. We also define a mapping function, which converts the sentiment probability from the classifier to a sentiment score used in our fusion function. At the same time, for the accompanied textual annotations, we employ the Synset forest to extract the sets of the meaningful sentiment words and utilize the SentiWordNet3.0 model to obtain the textual sentiment score. Then, we design a joint visual-textual sentiment score function weighted with visual sentiment component and textual sentiment one. To make the function more robust, we introduce a noticeable difference threshold to further process the fused sentiment score.Finally, we adopt grid search technique to obtain the relevant model hyper-parameters by optimizing a sentiment aware score function.Experimental results and analysis extensively demonstrate the effectiveness of the proposed sentiment recognition scheme on three benchmark datasets including T-GIF dataset, GSO-2016 dataset and Adjusted-GIFGIF dataset.

In recent years, small object detection has been widely applied to aerial scenes. Although existing small object detection algorithms have achieved significant success, it remains challenging to ensure an acceptable processing speed and detection accuracy simultaneously in high-resolution images. In this work, we propose a novel framework called SliNet, a slicingaided learning network with a SPPFCSPC Block and a ContentAware ReAssembly of Features (CARAFE) Block merged. Since aerial images usually have high resolutions and small detection targets, we slice images into smaller overlapping patches and integrate SPPFCSPC and CARAFE to find attention region with larger receptive field in dense-object scenes. Experimental results on dataset VisDrone-DET2019 show that the SliNet achieves competitive performance and obtains faster detection speed due to the decrease of computational cost. On the VisDrone2019- DET-val dataset, we attain a mAP score of 46.4% with a mAP50 score of 67.1%, which is better than the state-of-the-art models, demonstrating the superiority of our approach

Images captured under low‐light conditions are often of low visibility. To improve visualisation, a novel low light image enhancement method is presented based on the non‐uniform illumination prior model. First, the k ‐means method is used to process the value channel in the hue‐saturation‐value (HSV) colour space after space conversion of the input image. Then, the initial illumination of segmented scenes is estimated by an improved maximum red–green–blue method. Next, an illumination preservation method is presented to maintain the naturalness of the enhanced image. Furthermore, the non‐uniform illumination prior model is proposed to enhance the textural details in the enhanced image. Fast Fourier transformation is used to accelerate the optimisation. Since an adaptive weight is assigned, the proposed method can preserve the edges and textures at the bright and edge areas. Experimental analysis shows that the results using the proposed method have less noise, better illumination, improved contrast, and satisfactory naturalness. In addition, the proposed method can provide better quality images in terms of subjective and objective assessments.