Composed image retrieval, a task involving the search for a target image
using a reference image and a complementary text as the query, has witnessed
significant advancements owing to the progress made in cross-modal modeling.
Unlike the general image-text retrieval problem with only one alignment
relation, i.e., image-text, we argue for the existence of two types of
relations in composed image retrieval. The explicit relation pertains to the
reference image & complementary text-target image, which is commonly exploited
by existing methods. Besides this intuitive relation, the observations during
our practice have uncovered another implicit yet crucial relation, i.e.,
reference image & target image-complementary text, since we found that the
complementary text can be inferred by studying the relation between the target
image and the reference image. Regrettably, existing methods largely focus on
leveraging the explicit relation to learn their networks, while overlooking the
implicit relation. In response to this weakness, We propose a new framework for
composed image retrieval, termed dual relation alignment, which integrates both
explicit and implicit relations to fully exploit the correlations among the
triplets. Specifically, we design a vision compositor to fuse reference image
and target image at first, then the resulted representation will serve two
roles: (1) counterpart for semantic alignment with the complementary text and
(2) compensation for the complementary text to boost the explicit relation
modeling, thereby implant the implicit relation into the alignment learning.
Our method is evaluated on two popular datasets, CIRR and FashionIQ, through
extensive experiments. The results confirm the effectiveness of our
dual-relation learning in substantially enhancing composed image retrieval
performance.

我们提出了一种新的合成图像检索框架，名为双关系对齐，它将显式和隐式关系相结合，充分利用三元组之间的相关性，通过融合参考图像和目标图像设计了一个视觉组合器，显著提升了合成图像检索性能。

组合图像检索的双重关系对齐

Dual Relation Alignment for Composed Image Retrieval

Time series forecasting plays a key role in a variety of domains. In a lot of
real-world scenarios, there exist multiple forecasting entities (e.g. power
station in the solar system, stations in the traffic system). A straightforward
forecasting solution is to mine the temporal dependency for each individual
entity through 1d-CNN, RNN, transformer, etc. This approach overlooks the
relations between these entities and, in consequence, loses the opportunity to
improve performance using spatial-temporal relation. However, in many
real-world scenarios, beside explicit relation, there could exist crucial yet
implicit relation between entities. How to discover the useful implicit
relation between entities and effectively utilize the relations for each entity
under various circumstances is crucial. In order to mine the implicit relation
between entities as much as possible and dynamically utilize the relation to
improve the forecasting performance, we propose an attentional multi-graph
neural network with automatic graph learning (A2GNN) in this work.
Particularly, a Gumbel-softmax based auto graph learner is designed to
automatically capture the implicit relation among forecasting entities. We
further propose an attentional relation learner that enables every entity to
dynamically pay attention to its preferred relations. Extensive experiments are
conducted on five real-world datasets from three different domains. The results
demonstrate the effectiveness of A2GNN beyond several state-of-the-art methods.

本文提出了一种基于自动图学习的注意力多图神经网络 (A2GNN)，旨在挖掘时间序列多实体预测中的显式和隐式关系，并使每个实体动态注意其首选关系，实验表明 A2GNN 优于现有的一些最新方法。