In recent years, the field of Natural Language Processing (NLP) has undergone tremendous changes, thanks in large part to the emergence of Large Language Models (LLMs). Represented by models such as OpenAI's ChatGPT and GPT-4, these LLMs have demonstrated remarkable abilities in understanding and generating human-like text. Building upon this foundation, the field of Multimodal Large Language Models (MLLMs) has gradually gained prominence, combining text understanding with visual comprehension to bring about new breakthroughs in artificial intelligence development. However, a major challenge faced by MLLMs is how to effectively integrate visual information. Currently, some MLLMs such as MiniGPT-4 and LLaVA are able to utilize image information, but they are typically limited to processing low-resolution images, which restricts their ability to discern subtle details. On the other hand, models like Monkey and OtterHD can handle high-resolution images, but they are susceptible to irrelevant details. Therefore, finding a balance between global context and local information has become crucial for the development of MLLMs. To address this issue, researchers have proposed a strategy called DualFocus, inspired by the human cognitive process, for MLLMs. This strategy mimics how humans typically scan an image globally and then focus on relevant details to answer questions. Specifically, DualFocus first analyzes the entire image to grasp the macro context, and then identifies important regions to zoom in for detailed examination. This strategy corresponds to the Chain of Thought (CoT) in NLP, enabling MLLMs to simultaneously process both micro and macro perspectives in images by incorporating visual cues into the cognitive sequence. To implement the DualFocus strategy, researchers carefully selected and curated a new dataset from Visual Genome (VG). During the model training phase, MLLMs learn to identify the relevant coordinates defining any query's important subregions. During the inference phase, the model generates two potential answers using macro and micro answer paths. Finally, the best response is selected by comparing the computed losses from the two answers based on perplexity (PPL) as the decision metric. Experimental evaluations have shown that the DualFocus strategy performs exceptionally well on various benchmark tests. Equipped with DualFocus, MLLMs have demonstrated significant improvements in performance compared to baseline models such as LLaVA 1.5 and Qwen-VL-Chat. Furthermore, the reduction in hallucinatory responses in benchmark tests like POPE highlights the potential of this framework in maintaining a balanced perspective when generating text. These findings emphasize the generality and effectiveness of the DualFocus mechanism in enhancing the capabilities of MLLMs across various tasks and datasets. In conclusion, the adoption of the DualFocus strategy represents a significant advancement in the field of multimodal language understanding. By seamlessly and efficiently integrating visual and textual processing, MLLMs equipped with this mechanism have enhanced performance across a range of tasks, from traditional Visual Question Answering (VQA) benchmarks to more complex multimodal challenges. Additionally, the success of DualFocus in mitigating hallucinatory responses provides strong support for its potential in improving model prediction accuracy and enhancing the credibility and reliability of AI-generated content. As research in this field continues to deepen and evolve, the DualFocus framework is poised to become an important avenue for more complex and nuanced interactions between language and vision in future artificial intelligence systems.