Abstract
Pre-training weakly related image-text pairs in the contrastive style shows great power in learning semantic aligning cross-modal models. The common choice to measure the distance between the feature representations of the image-text pairs is the cosine similarity, which can be considered as the negative inner product of features embedded on a sphere mathematically. While such topology benefits from the low computational resources consumption and a properly defined uniformity, typically, there are two major drawbacks when applied. First, it is vulnerable to the semantic ambiguity phenomenon resulting from the noise in the weakly-related image-text pairs. Second, the learning progress is unstable and fragile at the beginning. Although, in the practice of former studies, a learnable softmax temperature parameter and a long warmup scheme are employed to meliorate the training progress, still there lacks an in-depth analysis of these problems. In this work, we discuss the desired properties of the topology and its endowed distance function for the embedding vectors of feature representations from the view of optimization. We then propose a rather simple solution to improve the aforementioned problem. That is, we map the feature representations onto the oblique manifold endowed with the negative inner product as the distance function. In the experimental analysis, we show that we can improve the baseline performance by a large margin (e.g. 4% in the zero-shot image to text retrieval task) by changing only two lines of the training codes.
Abstract (translated)
URL
https://arxiv.org/abs/2209.02127
