In current multimodal tasks, models typically freeze the encoder and decoder while adapting intermediate layers to task-specific goals, such as region captioning. Region-level visual understanding presents significant challenges for large-scale vision-language models. While limited spatial awareness is a known issue, coarse-grained pretraining, in particular, exacerbates the difficulty of optimizing latent representations for effective encoder-decoder alignment. We propose AlignCap, a framework designed to enhance region-level understanding through fine-grained alignment of latent spaces. Our approach introduces a novel latent feature refinement module that enhances conditioned latent space representations to improve region-level captioning performance. We also propose an innovative alignment strategy, the semantic space alignment module, which boosts the quality of multimodal representations. Additionally, we incorporate contrastive learning in a novel manner within both modules to further enhance region-level captioning performance. To address spatial limitations, we employ a General Object Detection (GOD) method as a data preprocessing pipeline that enhances spatial reasoning at the regional level. Extensive experiments demonstrate that our approach significantly improves region-level captioning performance across various tasks

翻译：在当前多模态任务中，模型通常冻结编码器和解码器，同时使中间层适应特定任务目标，例如区域描述。区域级视觉理解对大规模视觉语言模型提出了重大挑战。虽然有限的空间感知能力是一个已知问题，但特别是粗粒度预训练加剧了优化潜在表征以实现有效编码器-解码器对齐的难度。我们提出了AlignCap，一个旨在通过潜在空间的细粒度对齐来增强区域级理解的框架。我们的方法引入了一种新颖的潜在特征精炼模块，该模块增强了条件化潜在空间表征，以提升区域级描述性能。我们还提出了一种创新的对齐策略，即语义空间对齐模块，该模块提升了多模态表征的质量。此外，我们在两个模块中以新颖的方式融入了对比学习，以进一步增强区域级描述性能。为了解决空间限制，我们采用了一种通用目标检测（GOD）方法作为数据预处理流程，以增强区域层面的空间推理能力。大量实验表明，我们的方法在各种任务中显著提升了区域级描述性能。