Selectively Hard Negative Mining for Alleviating Gradient Vanishing in Image-Text Matching

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 at https://github.com/AAA-Zheng/RVSEPP.