Dense multi-label action detection is a challenging task in the field of visual action, where multiple actions occur simultaneously in different time spans, hence accurately assessing the short-term and long-term temporal dependencies between actions is crucial for action detection. There is an urgent need for an effective temporal modeling technology to detect the temporal dependence of actions in videos and efficiently learn long-term and short-term action information. This paper proposes a new method based on temporal pyramid and long short-term time modeling for multi-label action detection, which combines hierarchical structure with pyramid feature hierarchy for dense multi-label temporal action detection. By using the expansion and compression convolution module (SEC) and external attention for time modeling, we focus on the temporal relationships of long and short-term actions at each stage. We then integrate hierarchical pyramid features to achieve accurate detection of actions at different temporal resolution scales. We evaluated the performance of the model on dense multi-label benchmark datasets, and achieved mAP of 47.3% and 36.0% on the MultiTHUMOS and TSU datasets, which outperforms 2.7% and 2.3% on the current state-of-the-art results. The code is available at https://github.com/Yoona6371/TP-LSM.

The rich information and complex background of industrial images make it a challenging task to improve the high compression rate of images. Current learning-based image compression methods mostly use customized convolutional neural networks (CNNs), which find it difficult to cope with the complex production background of industrial images. This causes useful information to be lost in the abundance of irrelevant data, making it difficult to accurately extract important features during the feature extraction stage. To address this, a Multi-path Residual Asymmetric Convolutional Compression Network (MRACNN) is proposed. Firstly, a Multi-path Residual Asymmetric Convolution Block (MRACB) is introduced, which includes the Multi-path Residual Asymmetric Convolution Down-sampling Module for down-sampling in the encoder to extract key features, and the Mult-path Residual Asymmetric Convolution Up-sampling Module for up-sampling in the decoder to recover details and reconstruct the image. This feature transfer and information flow enables the better capture of image details and important information, thereby improving the quality and efficiency of image compression and decompression. Furthermore, a two-branch enhanced local attention mechanisms, and a channel-squeezing entropy model based on the compression-based enhanced local attention module is proposed to enhance the performance of the modeled compression. Extensive experimental evaluations demonstrate that the proposed method outperforms state-of-the-art techniques, achieves superior Rate–Distortion Performance, and excels in preserving local details.