In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.

Anomaly detection in crowded scenes is an important and challenging part of the intelligent video surveillance system. As the deep neural networks make success in feature representation, the features extracted by a deep neural network represent the appearance and motion patterns in different scenes more specifically, comparing with the hand-crafted features typically used in the traditional anomaly detection approaches. In this paper, we propose a new baseline framework of anomaly detection for complex surveillance scenes based on a variational auto-encoder with convolution kernels to learn feature representations. Firstly, the raw frames series are provided as input to our variational auto-encoder without any preprocessing to learn the appearance and motion features of the receptive fields. Then, multiple Gaussian models are used to predict the anomaly scores of the corresponding receptive fields. Our proposed two-stage anomaly detection system is evaluated on the video surveillance dataset for a large scene, UCSD pedestrian datasets, and yields competitive performance compared with state-of-the-art methods.

Underwater Image Enhancement (UIE) aims to restore image quality by mitigating inherent degradations in underwater imaging systems. While existing learning-based methods show promise, they face limitations in separating and processing frequency components, effectively fusing domain information, and balancing the enhancement of structures and details. To resolve these limitations, we propose CDF-UIE, a novel network that leverages and fuses cross-domain information for mitigating the degradation in underwater images. CDF-UIE first performs domain decoupling of input features using the proposed Spatial-Frequency Decoupling (SFD) block. Then we design an innovative Cross-Domain Fusion (CDF) block, which effectively bridges the spatial and frequency domain features through the cross-domain attention mechanism. To produce stable and detailed enhanced outputs, we exploit the coarse and fine-scale information in the image reconstruction stage. Additionally, we introduce a multiscale objective function that incorporates pixel-level, structural, and perceptual constraints to guide the enhancement process. We conduct extensive experiments on six diverse real-world underwater image datasets. Comprehensive experiments and real-world application tests demonstrate that CDF-UIE significantly outperforms existing methods, offering promising future applications in various underwater scenarios. The source code is available at https://github.com/hpzhan66/CDF-UIE.

At present, the traditional machine learning methods and convolutional neural network (CNN) methods are mostly used in image recognition. The feature extraction process in traditional machine learning for image recognition is mostly executed by manual, and its generalization ability is not strong enough. The earliest convolutional neural network also has many defects, such as high hardware requirements, large training sample size, long training time, slow convergence speed and low accuracy. To solve the above problems, this paper proposes a novel deep LeNet-5 convolutional neural network model for image recognition. On the basis of Lenet-5 model with the guaranteed recognition rate, the network structure is simplified and the training speed is improved. Meanwhile, we modify the Logarithmic Rectified Linear Unit (L ReLU) of the activation function. Finally, the experiments are carried out on the MINIST character library to verify the improved network structure. The recognition ability of the network structure in different parameters is analyzed compared with the state-of-the-art recognition algorithms. In terms of the recognition rate, the proposed method has exceeded 98%. The results show that the accuracy of the proposed structure is significantly higher than that of the other recognition algorithms, which provides a new reference for the current image recognition.