Transductive Information Maximization (TIM) is a leading transductive few-shot learning method that maximizes the mutual information between query features and their predicted labels, while incorporating supervision from the support set. However, its potential remains underexplored, primarily due to the limited utilization of textual knowledge provided by vision-language models (VLMs) such as CLIP. To address this, we propose TIM++, an enhanced framework that incorporates both visual and textual information for few-shot CLIP adaptation. Specifically, TIM++ introduces a Kullback-Leibler (KL) divergence-based regularization term that encourages the model’s posterior predictions to align with CLIP’s zero-shot output distribution, especially focusing on the most confident predictions. Additionally, we develop an improved prototype initialization strategy that leverages both support and query features enriched with CLIP-guided semantics. Extensive experiments on 11 public datasets demonstrate that TIM++ consistently outperforms the standard TIM, achieving average accuracy gains of 19.25% and 10.88% in 1-shot and 2-shot settings, respectively. TIM++ also surpasses other existing state-of-the-art methods, establishing a new benchmark for few-shot learning with VLMs.

Particle Swarm Optimization (PSO) is a popular gradient-free meta-heuristic, but its many variants pose significant selection and tuning challenges for non-specialists. We introduce Automated Deep-learning-based Particle Swarm Optimization (AD-PSO), a framework that highly automates PSO configuration and execution. AD-PSO features a modular design, incorporating diverse PSO variants as interchangeable components.

Many Particle Swarm Optimization (PSO) variants often fail to approach the global optimum due to complex fitness landscapes and/or the structural rigidity of these variants. We developed a novel framework named PSO-SEM, centered on macro-evolutionary population management. PSO-SEM introduces three landscape-driven operators: Split (Topological Fission), Extinction (Density-driven Recycling), and Migration (Knowledge Transfer). These operators autonomously regulate the lifecycle of sub-swarms to intensify search in high-potential areas by identifying promising basins and recycling computational resources from stagnant regions. Experimental results show that PSO-SEM achieves a top-tier ranking and demonstrates significant competitiveness against 14 state-of-the-art algorithms. Behavioral monitoring and ablation studies confirm PSO-SEM's ability to maintain autonomous exploration/exploitation balance through landscape-based computational resource re-allocation. Our findings verify that PSO-SEM is an interpretable architecture that meets the diverse requirements of black-box optimization.

The key to improve the service effect of virtual digital human is the personality trait recognition technology of users. However, the complexity of Chinese and the sparsity of short texts limit the accuracy of personality trait recognition. Therefore, we applied emotional features to the research of personality trait recognition and constructed a deep learning model for Chinese corpus personality trait recognition to improve the accuracy of personality trait recognition. The model ([Formula: see text] extracted semantic features and embedded emotional features through RoBERTa-wwm-ext Chinese pre-training model, BiLSTM neural network, and attention mechanism. Some experiments were carried out using two Chinese datasets with different scenes. The comparative experiments and ablation experiments showed that the overall effect of the personality trait recognition model ([Formula: see text] is the best, and the Macro-F1 value reaches 75.3%, which is significantly better than the existing models. The results showed that emotional features can significantly improve the accuracy of multi-label personality trait classification in both Chinese daily life scenarios and online scenarios. The proposed model can integrate emotional features and Chinese semantic features to improve the accuracy of personality traits recognition. These findings support the development of personality trait recognition and the application of virtual digital human.