CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning

Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. This knowledge, typically encapsulated in attribute descriptions, aids in identifying class-specific visual features, thus facilitating visual-semantic alignment and improving ZSL performance. However, real-world challenges such as distribution imbalances and attribute co-occurrence among instances often hinder the discernment of local variances in images, a problem exacerbated by the scarcity of fine-grained, region-specific attribute annotations. Moreover, the variability in visual presentation within categories can also skew attribute-category associations. In response, we propose a bidirectional cross-modal ZSL approach CREST. It begins by extracting representations for attribute and visual localization and employs Evidential Deep Learning (EDL) to measure underlying epistemic uncertainty, thereby enhancing the model's resilience against hard negatives. CREST incorporates dual learning pathways, focusing on both visual-category and attribute-category alignments, to ensure robust correlation between latent and observable spaces. Moreover, we introduce an uncertainty-informed cross-modal fusion technique to refine visual-attribute inference. Extensive experiments demonstrate our model's effectiveness and unique explainability across multiple datasets. Our code and data are available at: Comments: Ongoing work; 10 pages, 2 Tables, 9 Figures; Repo is available at https://github.com/JethroJames/CREST.

Ultra-short-term multi-step wind speed prediction for wind farms based on adaptive noise reduction technology and temporal convolutional network

As an important clean and renewable kind of energy, wind power plays an important role in coping with energy crisis and environmental pollution. However, the volatility and intermittency of wind speed restrict the development of wind power. To improve the utilization of wind power, this study proposes a new wind speed prediction model based on data noise reduction technology, temporal convolutional network (TCN), and gated recurrent unit (GRU). Firstly, an adaptive data noise reduction algorithm P-SSA is proposed based on singular spectrum analysis (SSA) and Pearson correlation coefficient. The original wind speed is decomposed into multiple subsequences by SSA and then reconstructed. When the Pearson correlation coefficient between the reconstructed sequence and the original sequence is greater than 0.99, other noise subsequences are deleted to complete the data denoising. Then, the receptive field of the samples is expanded through the causal convolution and dilated convolution of TCN, and the characteristics of wind speed change are extracted. Then, the time feature information of the sequence is extracted by GRU, and then the wind speed is predicted to form the wind speed sequence prediction model of P-SSA-TCN-GRU. The proposed model was validated on three wind farms in Shandong Province. The experimental results show that the prediction performance of the proposed model is better than that of the traditional model and other models based on TCN, and the wind speed prediction of wind farms with high precision and strong stability is realized. The wind speed predictions of this model have the potential to become the data that support the operation and management of wind farms. The code is available at link.