An object tracking framework with recapture based on correlation filters and Siamese networks

A context hierarchical integrated network for medical image segmentation

MCI-Net: Multi-scale context integrated network for liver CT image segmentation

YOLOv4_Drone: UAV image target detection based on an improved YOLOv4 algorithm

Deep hierarchical LSTM networks with attention for video summarization

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU, BLEU, METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

Huimin Lu

Papers

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM