A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL). While this approach can bring significant improvements, it usually presumes that an entire matrix of test samples is available as DSL input. This work introduces a new postprocessing approach based on Sinkhorn transformations that outperforms DSL. Further, we propose a new postprocessing setting that does not require access to multiple test queries. We show that our approach can significantly improve the results of state of the art models such as CLIP4Clip, BLIP, X-CLIP, and DRL, thus achieving a new state-of-the-art on several standard text-video retrieval datasets both with access to the entire test set and in the single-query setting.

基于Sinkhorn变换的新型后处理方法在多模态检索中取得了比双softmax损失更好的效果，并提出了一种新的后处理设置，不需要访问多个测试查询，可以显著提高CLIP4Clip、BLIP、X-CLIP和DRL等先进模型在多个标准文本-视频检索数据集上的结果，在整个测试集和单查询设置中均达到了新的最先进水平。

文本-视频检索中的单查询后处理的Sinkhorn变换