Zhuoyang Li

Qiannan Fan, Zhuoyang Li, Jitong Li et al.

With the continuous impact of epidemics, people have become accustomed to wearing masks. However, most current occluded face recognition (OFR) algorithms lack prior knowledge of occlusions, resulting in poor performance when dealing with occluded faces of varying types and severity in reality. Recognizing occluded faces is still a significant challenge, which greatly affects the convenience of people's daily lives. In this paper, we propose an identity-gated mixture of diffusion experts (MoDE) for OFR. Each diffusion-based generative expert estimates one possible complete image for occluded faces. Considering the random sampling process of the diffusion model, which introduces inevitable differences and variations between the inpainted faces and the real ones. To ensemble effective information from multi-reconstructed faces, we introduce an identity-gating network to evaluate the contribution of each reconstructed face to the identity and adaptively integrate the predictions in the decision space. Moreover, our MoDE is a plug-and-play module for most existing face recognition models. Extensive experiments on three public face datasets and two datasets in the wild validate our advanced performance for various occlusions in comparison with the competing methods.

Zhuoyang Li, Liran Deng, Hui Liu et al.

OwnThink stands as the most extensive Chinese open-domain knowledge graph introduced in recent times. Despite prior attempts in question answering over OwnThink (OQA), existing studies have faced limitations in model representation capabilities, posing challenges in further enhancing overall accuracy in question answering. In this paper, we introduce UniOQA, a unified framework that integrates two complementary parallel workflows. Unlike conventional approaches, UniOQA harnesses large language models (LLMs) for precise question answering and incorporates a direct-answer-prediction process as a cost-effective complement. Initially, to bolster representation capacity, we fine-tune an LLM to translate questions into the Cypher query language (CQL), tackling issues associated with restricted semantic understanding and hallucinations. Subsequently, we introduce the Entity and Relation Replacement algorithm to ensure the executability of the generated CQL. Concurrently, to augment overall accuracy in question answering, we further adapt the Retrieval-Augmented Generation (RAG) process to the knowledge graph. Ultimately, we optimize answer accuracy through a dynamic decision algorithm. Experimental findings illustrate that UniOQA notably advances SpCQL Logical Accuracy to 21.2% and Execution Accuracy to 54.9%, achieving the new state-of-the-art results on this benchmark. Through ablation experiments, we delve into the superior representation capacity of UniOQA and quantify its performance breakthrough.