Abstract
Image captioning, like many tasks involving vision and language, currently relies on Transformer-based architectures for extracting the semantics in an image and translating it into linguistically coherent descriptions. Although successful, the attention operator only considers a weighted summation of projections of the current input sample, therefore ignoring the relevant semantic information which can come from the joint observation of other samples. In this paper, we devise a network which can perform attention over activations obtained while processing other training samples, through a prototypical memory model. Our memory models the distribution of past keys and values through the definition of prototype vectors which are both discriminative and compact. Experimentally, we assess the performance of the proposed model on the COCO dataset, in comparison with carefully designed baselines and state-of-the-art approaches, and by investigating the role of each of the proposed components. We demonstrate that our proposal can increase the performance of an encoder-decoder Transformer by 3.7 CIDEr points both when training in cross-entropy only and when fine-tuning with self-critical sequence training. Source code and trained models are available at: this https URL.
Abstract (translated)
图像标题与许多涉及视觉和语言的任务的Task一样,目前依赖于基于Transformer的架构提取图像语义并将它翻译成语言 coherent 的描述。尽管取得了成功,但注意力操作只考虑当前输入样本 projections 的加权总和,因此忽略了可以从其他样本 joint 观察的相关语义信息。在本文中,我们设计了一种网络,可以在处理其他训练样本时对获取的激活进行注意力处理,通过创建一个原型记忆模型。我们的记忆模型通过定义具有区分性和紧凑性的原型向量来定义过去的键和值分布。实验中,我们比较了精心设计的基准线和当前方法的性能,并研究了每个 proposed 组件的作用。我们证明了,我们的提议可以在仅使用交叉熵训练的情况下,或在使用自我关键序列训练的情况下,提高编码器和解码器 Transformer 的性能,从而提高了3.7 CIDEr 点。源代码和训练模型可在以下httpsURL上获取。
URL
https://arxiv.org/abs/2308.12383
