一种双重对比框架
A dual contrastive framework
AI总结：
针对大规模视觉-语言模型区域级理解难的问题，提出AlignCap双重对比框架，通过潜在特征细化、语义空间对齐模块结合对比学习，并辅以GOD预处理增强空间推理，显著提升多任务区域字幕生成性能。
摘要：
在当前的多模态任务中，模型通常会冻结编码器和解码器，同时调整中间层以适配特定任务目标，例如区域字幕生成。区域级视觉理解对大规模视觉-语言模型提出了重大挑战。尽管空间感知能力有限是一个已知问题，但粗粒度预训练尤其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，这是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细化模块，该模块可增强条件化潜在空间表征，以提升区域级字幕生成性能。我们还提出了一种创新的对齐策略，即语义空间对齐模块，它能够提高多模态表征的质量。此外，我们以一种新颖的方式在两个模块中结合了对比学习，以进一步提升区域级字幕生成性能。为了解决空间局限性，我们采用通用目标检测（General Object Detection, GOD）方法作为数据预处理流水线，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字幕生成的性能。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
