MCDisp-Align: Multi-Caption Semantic Dispersion-Guided Distribution Alignment for One-to-Many Vision-Language Representation Learning

Vision-language representation learning supports a wide range of multimodal applications by aligning visual and textual semantics, but existing methods still face two key challenges. Deterministic methods embed samples as single points, which tends to compress differences among semantically distinct descriptions and cause semantic distortion. Conversely, while probabilistic methods model samples as distributions, aligning a broader image distribution with caption distributions that primarily contain semantically similar points can restrict the image's semantic range, leading to semantic collapse. To address these challenges, we propose MCDisp-Align, a probabilistic framework that leverages multi-caption semantic dispersion to guide image distribution learning. MCDisp-Align constructs a caption-set distribution that captures the shared semantic center, semantic range, and dominant directions of variation across captions. By combining set-level distribution alignment with caption-level coverage, it preserves semantic differences among individual captions to reduce semantic distortion, while guiding the image distribution to cover complementary semantic perspectives and mitigate semantic collapse. We evaluate MCDisp-Align on image-text retrieval along with geometric analyses on MSCOCO and Flickr30K. The results demonstrate improved retrieval performance alongside greater semantic dispersion among caption representations of the same image and clearer separation across images, effectively balancing semantic coverage and discrimination. Code will be made publicly available upon acceptance.