Dingxian Wang

Jiaqi Deng, Kaize Shi, Zonghan Wu et al.

Knowledge-based Vision Question Answering (KB-VQA) systems address complex visual-grounded questions with knowledge retrieved from external knowledge bases. The tasks of knowledge retrieval and answer generation tasks both necessitate precise multimodal understanding of question context and external knowledge. However, existing methods treat these two stages as separate modules with limited interaction during training, which hinders bi-directional parametric knowledge sharing, ultimately leading to suboptimal performance. To fully exploit the cross-task synergy in KB-VQA, we propose a unified retrieval-augmented VQA framework with collaborative parametric knowledge calibration. The proposed framework can effectively adapt general multimodal pre-trained models for fine-grained, knowledge-intensive tasks while enabling the retriever and generator to collaboratively enhance and share their parametric knowledge during both training and inference. To enhance fine-grained understanding of questions and external documents, we also integrate late interaction mechanism into the proposed training framework. Additionally, we introduce a reflective-answering mechanism that allows the model to explicitly evaluate and refine its knowledge boundary. Our approach achieves competitive performance against state-of-the-art models, delivering a significant 4.7\% improvement in answering accuracy, and brings an average 7.5\% boost in base MLLMs' VQA performance.

Xiang Wang, Dingxian Wang, Canran Xu et al.

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.