Linguistic-Driven Partial Semantic Relevance Learning for Skeleton-Based Action Recognition.

Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton features, overlooking the potential semantic relationships among various partial limb motions. For instance, the subtle differences between actions such as "brush teeth" and "brush hair" are mainly distinguished by specific elements. Although combining limb movements provides a more holistic representation of an action, relying solely on skeleton points proves inadequate for capturing these nuances. Therefore, integrating detailed linguistic descriptions into the learning process of skeleton features is essential. This motivates us to explore integrating fine-grained language descriptions into the learning process of skeleton features to capture more discriminative skeleton behavior representations. To this end, we introduce a new Linguistic-Driven Partial Semantic Relevance Learning framework (LPSR) in this work. While using state-of-the-art large language models to generate linguistic descriptions of local limb motions and further constrain the learning of local motions, we also aggregate global skeleton point representations and textual representations (which generated from an LLM) to obtain a more generalized cross-modal behavioral representation. On this basis, we propose a cyclic attentional interaction module to model the implicit correlations between partial limb motions. Numerous ablation experiments demonstrate the effectiveness of the method proposed in this paper, and our method also obtains state-of-the-art results.

Research on Transportation Mode Recognition Based on Multi-Head Attention Temporal Convolutional Network.

Transportation mode recognition is of great importance in analyzing people's travel patterns and planning urban roads. To make more accurate judgments on the transportation mode of the user, we propose a deep learning fusion model based on multi-head attentional temporal convolution (TCMH). First, the time-domain features of a more extensive range of sensor data are mined through a temporal convolutional network. Second, multi-head attention mechanisms are introduced to learn the significance of different features and timesteps, which can improve the identification accuracy. Finally, the deep-learned features are fed into a fully connected layer to output the classification results of the transportation mode. The experimental results demonstrate that the TCMH model achieves an accuracy of 90.25% and 89.55% on the SHL and HTC datasets, respectively, which is 4.45% and 4.70% higher than the optimal value in the baseline algorithm. The model has a better recognition effect on transportation modes.

Compositional action recognition with multi-view feature fusion.

Most action recognition tasks now treat the activity as a single event in a video clip. Recently, the benefits of representing activities as a combination of verbs and nouns for action recognition have shown to be effective in improving action understanding, allowing us to capture such representations. However, there is still a lack of research on representational learning using cross-view or cross-modality information. To exploit the complementary information between multiple views, we propose a feature fusion framework, and our framework is divided into two steps: extraction of appearance features and fusion of multi-view features. We validate our approach on two action recognition datasets, IKEA ASM and LEMMA. We demonstrate that multi-view fusion can effectively generalize across appearances and identify previously unseen actions of interacting objects, surpassing current state-of-the-art methods. In particular, on the IKEA ASM dataset, the performance of the multi-view fusion approach improves 18.1% over the performance of the single-view approach on top-1.

[Dynamics of recent cultivated land in Zhejiang Province and relevant driving factors].

Through the human-computer interactive interpretation of the 2000, 2005, and 2008 remote sensing images of Zhejiang Province with the help of RS and GIS techniques, the dynamic database of cultivated land change in the province in, 2000-2008 was established, and the driving factors of the cultivated land change were analyzed by ridge regression analysis. There was a notable cultivated land change in the province in 2000-2008. In 2000-2005 and 2005-2008, the annual cultivated land change in the province arrived -1.42% and -1.46%, respectively, and most of the cultivated land was changed into residential and industrial land. Non-agricultural population rate, real estate investment, urban green area, and orchard area were thought to be the main driving factors of the cultivated land change in Zhejiang Province, and even, in the developed areas of east China.