A dual contrastive framework

TL;DR

This paper introduces AlignCap, a novel framework that enhances region-level understanding in vision-language models through fine-grained latent space alignment, contrastive learning, and spatial reasoning improvements, leading to better captioning results.

Contribution

AlignCap presents a new latent feature refinement module and semantic space alignment strategy, integrating contrastive learning and spatial reasoning to improve region-level captioning in multimodal models.

Findings

Significant improvement in region-level captioning performance

Effective enhancement of spatial reasoning capabilities

Demonstrated robustness across various tasks

Abstract

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