Knowledge-based Visual Question Answering (KB-VQA) has surfaced as a critical task in advancing AI capabilities. Despite significant progress enabled by large language models (LLMs), there are still three major challenges: (1) flawed image captions cause unreliable reasoning; (2) noisy explicit knowledge can disrupts answering; and (3) massive LLMs scale is irreplaceable to robustness. To overcome these challenges, we develop a novel approach, Multi-Modal Refined Prompting (MMRP), which generates high-quality prompts tailored for LLMs. To tackle the first challenge, a multi-faceted image captioning strategy is employed to generate detailed, contextually relevant visual descriptions. In addition, we introduce a complementary knowledge retrieval and refinement strategy to deliver concise, contextually relevant knowledge, effectively overcoming the second challenge. These enhanced image captions and explicit knowledge are then integrated into a knowledge-infused in-context prompt, effectively activating the reasoning capabilities of LLMs. Importantly, MMRP eliminates reliance on massive LLMs and avoids the need for model fine-tuning, while achieving significant improvements in answer accuracy. Extensive evaluations on the widely-used OK-VQA benchmark against 22 baselines prove the superiority of MMRP, establishing a new state-of-the-art in KB-VQA.

Text-Based Person Search (TBPS) aims to retrieve target pedestrian images through language descriptions. However, the visual attributes and textual descriptions of different identities (pedestrians) tend to exhibit considerable similarity, leading to Similar Semantic Interference (SSI). To mitigate this issue, we propose the Adapting Cross-Modal Semantic Discrepancy (ACMSD) method, employing a cross-modal constraint approach to alleviate interference in model training. Specifically, we introduce the Consistent Constraint Alignment (CCA) strategy, which establishes both inter-modal alignment and intra-modal alignment, along with an Identity-Balanced Distribution (IBD) loss. This paradigm utilizes Cyclic Image-Text Contrastive to regularize the spatial distribution of the modalities, while the IBD loss implicitly clusters strong positive samples by using identity as a key index. Additionally, we incorporate an Attention-based Implicit Alignment (AIA) module to enforce modality-specific embeddings, thereby strengthening the interaction between cross-modal information. Extensive experiments are conducted on three public benchmark datasets to evaluate the performance of the ACMSD method.