Most Image-Text Matching (ITM) models adopt Triplet loss with Hard Negative mining (T-HN) as the optimization objective. T-HN mines the hardest negative samples in each batch for training and achieves impressive performance. However, we observe that these ITM models have bad training behaviors in the early phases of training. Model training is difficult to converge, and matching performance is slow to improve. In this paper, we find that the cause of bad training behavior is that the model suffers from gradient vanishing. Optimizing an ITM model using only the hardest negative samples can easily lead to gradient vanishing. Through gradient analysis, we first derive the condition under which the gradient vanishes during training. We explain why the gradient tends to zero under certain conditions. To alleviate gradient vanishing, we propose a Triplet loss with Selectively Hard Negative mining (T-SelHN), which decides whether to mine the hardest negative samples according to the gradient vanishing condition. T-SelHN can be applied to ITM models in a plug-and-play manner to improve their training behaviors. To further ensure the back-propagation of gradients, we construct a Residual Visual Semantic Embedding model with T-SelHN, denoted RVSE++, which has a simple network structure and efficient training and inference speeds. Extensive experiments on two ITM benchmarks demonstrate the strength of RVSE++, achieving state-of-the-art performance. The code is available athttps://github.com/AAA-Zheng/RVSEPP.

Image-Text Matching (ITM) aims to establish the correspondence between images and sentences. ITM is fundamental to various vision and language understanding tasks. However, there are limitations in the way existing ITM benchmarks are constructed. The ITM benchmark collects pairs of images and sentences during construction. Therefore, only samples that are paired at collection are annotated as positive. All other samples are annotated as negative. Many correlations are missed in these samples that are annotated as negative. For example, a sentence matches only one image at the time of collection. Only this image is annotated as positive for the sentence. All other images are annotated as negative. However, these negative images may contain images that correspond to the sentences. These mislabeled samples are calledfalse negatives. Existing ITM models are optimized based on annotations containing mislabels, which can introduce noise during training. In this paper, we propose an ITM framework integrating Language Guidance (LG) for correcting false negatives. A language pre-training model is introduced into the ITM framework to identify false negatives. To correct false negatives, we propose language guidance loss, which adaptively corrects the locations of false negatives in the visual-semantic embedding space. Extensive experiments on two ITM benchmarks show that our method can improve the performance of existing ITM models. To verify the performance of correcting false negatives, we conduct further experiments on ECCV Caption. ECCV Caption is a verified dataset where false negatives in annotations have been corrected. The experimental results show that our method can recall more relevant false negatives. The code is available athttps://github.com/AAA-Zheng/LG_ITM.