Current metric learning approaches for image retrieval are usually based on
learning a space of informative latent representations where simple approaches
such as the cosine distance will work well. Recent state of the art methods
such as HypViT move to more complex embedding spaces that may yield better
results but are harder to scale to production environments. In this work, we
first construct a simpler model based on triplet loss with hard negatives
mining that performs at the state of the art level but does not have these
drawbacks. Second, we introduce a novel approach for image retrieval
postprocessing called Siamese Transformer for Image Retrieval (STIR) that
reranks several top outputs in a single forward pass. Unlike previously
proposed Reranking Transformers, STIR does not rely on global/local feature
extraction and directly compares a query image and a retrieved candidate on
pixel level with the usage of attention mechanism. The resulting approach
defines a new state of the art on standard image retrieval datasets: Stanford
Online Products and DeepFashion In-shop. We also release the source code at
this https URL
and an interactive demo of our approach at
this https URL

该研究提出了一种基于三元损失的简化模型和一种用于图像检索的新方法 STIR，它采用注意力机制在像素级别上直接比较查询图像和候选图像，不需要全局 / 局部特征提取，能够在标准图像检索数据集上定义新的技术状态。

STIR: 用于图像检索后处理的孪生 Transformer

STIR: Siamese Transformer for Image Retrieval Postprocessing

This paper describes the second-placed system on the leaderboard of
SemEval-2022 Task 8: Multilingual News Article Similarity. We propose an
entity-enriched Siamese Transformer which computes news article similarity
based on different sub-dimensions, such as the shared narrative, entities,
location and time of the event discussed in the news article. Our system
exploits a Siamese network architecture using a Transformer encoder to learn
document-level representations for the purpose of capturing the narrative
together with the auxiliary entity-based features extracted from the news
articles. The intuition behind using all these features together is to capture
the similarity between news articles at different granularity levels and to
assess the extent to which different news outlets write about "the same
events". Our experimental results and detailed ablation study demonstrate the
effectiveness and the validity of our proposed method.

本文描述了 SemEval-2022 任务 8 排行榜上排名第二的系统。我们提出了一种基于实体增强的连体 Transformer，根据新闻文章中讨论的事件的共享叙述、实体、位置和时间等不同子维度计算新闻文章相似性。