Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks. Recent efforts have been made to equip MLLMs with visual perceiving and grounding capabilities. However, there still remains a gap in providing fine-grained pixel-level perceptions and extending interactions beyond text-specific inputs. In this work, we propose {\bf{AnyRef}}, a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references, such as texts, boxes, images, or audio. This innovation empowers users with greater flexibility to engage with the model beyond textual and regional prompts, without modality-specific designs. Through our proposed refocusing mechanism, the generated grounding output is guided to better focus on the referenced object, implicitly incorporating additional pixel-level supervision. This simple modification utilizes attention scores generated during the inference of LLM, eliminating the need for extra computations while exhibiting performance enhancements in both grounding masks and referring expressions. With only publicly available training data, our model achieves state-of-the-art results across multiple benchmarks, including diverse modality referring segmentation and region-level referring expression generation.

翻译：多模态大语言模型（MLLMs）以大型语言模型为认知框架，可处理多种视觉-语言任务。近期研究致力于赋予MLLMs视觉感知与定位能力，但当前在提供细粒度像素级感知以及扩展文本特定输入之外的交互方式方面仍存在差距。本文提出{\bf{AnyRef}}，一种通用多模态大语言模型，能够从文本、边界框、图像或音频等多模态参考中生成像素级对象感知与自然语言描述。该创新使用户无需针对特定模态进行设计，即可通过文本和区域提示之外的更灵活方式与模型交互。通过我们提出的重聚焦机制，生成的定位输出被引导至更精准聚焦参考对象，隐式引入额外像素级监督。这一简洁改进利用大语言模型推理过程中产生的注意力分数，在无需额外计算开销的同时，提升了定位掩码与指代表达的性能。仅使用公开训练数据，我们的模型在多项基准测试中取得领先结果，涵盖多模态指代分割与区域级指代表达生成等任务。