Grounded Multimodal Named Entity Recognition (GMNER) extends Multimodal Named Entity Recognition (MNER) by identifying named entities, their types, and corresponding image regions. Fine-grained MNER and Grounding (FMNERG) further refines entity categorization. However, existing methods struggle with scarce annotated data, particularly in low-resource scenarios, and often fail to generalize to unseen entities. While vision-language pre-training (VLP) leverages unlabeled image-caption pairs, it primarily learns generic visual-linguistic representations, overlooking fine-grained entity-region alignment crucial for entity-related tasks. To address these challenges, we propose a unified VLP framework for GMNER and FMNERG, introducing two task-specific pre-training objectives: Entity-to-Region Alignment (ETRA) for entity grounding and Region-to-Entity Alignment (RTEA) for entity reconstruction. These tasks jointly optimize fine-grained entity-region alignment. To compensate for the lack of fine-grained multimodal pre-training data, we develop an automatic labeling method that distills entity-oriented knowledge from large-scale unlabeled image-text pairs, enhancing generalization to unseen entities. Extensive experiments on GMNER and FMNERG benchmarks demonstrate that our framework outperforms existing low-resource learning approaches and achieves competitive performance in full-supervision, underscoring its effectiveness across diverse data conditions.

A hybrid model for social media popularity prediction is proposed by combining Convolutional Neural Network (CNN) with XGBoost. The CNN model is exploited to learn high-level representations from the social cues of the data. These high-level representations are used in XGBoost to predict the popularity of the social posts. We evaluate our approach on a real-world Social Media Prediction (SMP) dataset, which consists of 432K Flickr images. The experimental results show that the proposed approach is effective, achieving the following performance: Spearman's Rho: 0.7406, MSE: 2.7293, MAE: 1.2475.