Point-level weakly-supervised temporal action localization (P- TAL) aims to localize action instances in untrimmed videos through the use of single-point annotations in each instance. Existing methods predict the class activation se-quences without any boundary information, and the unreli-able sequences result in a significant misalignment between the quality of proposals and their corresponding confidence. In this paper, we surprisingly observe the most salientframe tend to appear in the central region of the each instance and is easily annotated by humans. Guided by the temporal saliency information, we present a novel proposal-level plug-in framework to relearn the aligned confidence of proposals generated by the base locators. The proposed approach consists of Center Score Learning (CSL) and Alignment-based Boundary Adaptation (ABA). In CSL, we design a novel center label generated by the point annotations for predicting aligned center scores. During inference, we first fuse the center scores with the predicted action probabilities to obtain the aligned confidence. ABA utilizes the both aligned confidence and IoU information to enhance localization completeness. Extensive experiments demon-strate the generalization and effectiveness of the proposed framework, showcasing state-of-the-art or competitive per-formances across three benchmarks. Our code is available at https://github.com/zyxial0091CVPR2024-TspNet.

Online action detection plays a vital role in video action understanding and can be widely used in various video analysis applications. This task aims to detect actions at the current moment within long untrimmed video streams. However, accurately identifying action-background transitions that are ambiguous in terms of time during detection can be challenging due to the similarity between the action and background clips, adding to the difficulty in finding a suitable division between them. To address this issue, we propose a hard video clip mining method based on deep metric learning for online action detection named HCM. The HCM method first selects video clips that are hard to distinguish to determine the optimization objects. Then, a hard clip mining loss is adopted to push the features toward the centers of the categories to which they belong and away from others. Furthermore, we introduce an intra-class feature compaction loss to constrain the divergence of action features, ensuring the stability of their distribution. We evaluated the proposed method on two challenging online action detection datasets, THUMOS14 and TVSeries. The results show that HCM is effective and efficient in online action detection and action anticipation tasks.

Although the convolutional neural network (CNN)-based and vision transformer (ViT)-based methods have achieved effective remote sensing scene classification results in the past few years, the CNN's inductive bias and the ViT's single spatial scale limit the further improvement of accuracy. To address these problems, in this paper, we proposed a novel multi-scale vision transformer (MS-ViT) for remote sensing scene image classification, consists of two different spatial scale information streams that extract spatial multi-scale information, a hybrid attention module that fuses the information and a classifier to predict the scene category. We evaluated the effectiveness of our proposed method on two different remote sensing datasets, namely NWPU-RESISC45 and AID. The experimental results also show that our method outperforms CNN-based methods and the original ViT-based method in performance.

Many practical applications of high-resolution remote sensing images (HRRSIs) are based on semantic segmentation. However, due to the complex ground object information contained in remote sensing images, it is difficult to make precise semantic segmentation of HRRSIs. In this letter, we proposed a hierarchical context aggregation network (HCANet) for the semantic segmentation of HRRSIs. The HCANet has an encoder-decoder structure which is similar to UNet. In the HCANet, we designed two Compact Atrous Spatial Pyramid Pooling (CASPP and CASPP+) modules. The CASPP modules replace the copy and crop operation in UNet to extract the multiscale context information of the multisemantic features of ResNet. The CASPP+ module is embedded in the middle layer of HCANet’s decoder to provide a strong aggregation path of contextual information. In the decoder of HCANet, the multiscale context information obtained by CASPP modules is hierarchically merged layer by layer for the semantic segmentation of HRRSIs. We compared our method with several of the most advanced methods on the ISPRS Vaihingen and Potsdam data sets. The final results demonstrate that our method can achieve outstanding performance.

Semantic segmentation of high-resolution remote sensing (HRRS) images becomes more and more important at present. Popular approaches use deep learning to solve this task, which depends on a large amount of labeled data and powerful computing resources. When computing resources or the labeled data are insufficient, their performance will be severely degraded. To deal with this problem, we proposed a light-weight network with attention modules for semantic segmentation of HRRS images. The depth and width of the network are designed, which has a small number of parameters to ensure the efficiency of training. The network adopts an encoder-decoder architecture. The feature maps of different scales from the encoder are concatenated together after resizing to carry out multi-scale feature fusion. To capture the global semantic information from the context, the attention mechanism is employed in the decoder. With one GTX2080Ti GPU and only 15 MB parameters the model owns, our light-weight network has quality results evaluated on ISPRS Vaihingen Dataset with fewer parameters compared to other popular approaches.