  Image-Text Matching (ITM) task, a fundamental vision-language (VL) task,
suffers from the inherent ambiguity arising from multiplicity and imperfect
annotations. Deterministic functions are not sufficiently powerful to capture
ambiguity, prompting the exploration of probabilistic embeddings to tackle the
challenge. However, the existing probabilistic ITM approach encounters two key
shortcomings; the burden of heavy computations due to the Monte Carlo
approximation, and the loss saturation issue in the face of abundant false
negatives. To overcome the issues, this paper presents an improved
Probabilistic Cross-Modal Embeddings (named PCME++) by introducing a new
probabilistic distance with a closed-form solution. In addition, two
optimization techniques are proposed to enhance PCME++ further: first, the
incorporation of pseudo-positives to prevent the negative effect under massive
false negatives; second, mixed sample data augmentation for probabilistic
matching. Experimental results on MS-COCO Caption and two extended benchmarks,
CxC and ECCV Caption, demonstrate the effectiveness of PCME++ compared to
state-of-the-art ITM methods. The robustness of PCME++ is also evaluated under
noisy image-text correspondences. In addition, the potential applicability of
PCME++ in automatic prompt-filtering for zero-shot classification is shown. The
code is available at https://github.com/naver-ai/pcmepp
