The pre-training task is indispensable for the text-to-image person
re-identification (T2I-ReID) task. However, there are two underlying
inconsistencies between these two tasks that may impact the performance; i)
Data inconsistency. A large domain gap exists between the generic images/texts
used in public pre-trained models and the specific person data in the T2I-ReID
task. This gap is especially severe for texts, as general textual data are
usually unable to describe specific people in fine-grained detail. ii) Training
inconsistency. The processes of pre-training of images and texts are
independent, despite cross-modality learning being critical to T2I-ReID. To
address the above issues, we present a new unified pre-training pipeline
(UniPT) designed specifically for the T2I-ReID task. We first build a
large-scale text-labeled person dataset "LUPerson-T", in which pseudo-textual
descriptions of images are automatically generated by the CLIP paradigm using a
divide-conquer-combine strategy. Benefiting from this dataset, we then utilize
a simple vision-and-language pre-training framework to explicitly align the
feature space of the image and text modalities during pre-training. In this
way, the pre-training task and the T2I-ReID task are made consistent with each
other on both data and training levels. Without the need for any bells and
whistles, our UniPT achieves competitive Rank-1 accuracy of, ie, 68.50%,
60.09%, and 51.85% on CUHK-PEDES, ICFG-PEDES and RSTPReid, respectively. Both
the LUPerson-T dataset and code are available at
https;//github.com/ZhiyinShao-H/UniPT.

该论文介绍了一种面向文本到图像人物重识别任务的统一预训练方法 (UniPT)，通过构建大规模的文本标注人物数据集、使用视觉 - 语言预训练框架来对齐图像和文本模态的特征空间，解决了数据不一致性和训练不一致性的问题。UniPT 在多个数据集上取得了有竞争力的准确率。

基于伪造文本的统一预训练技术用于文本 - 图像人物重识别

Unified Pre-training with Pseudo Texts for Text-To-Image Person  Re-identification

Data inconsistency and bias are inevitable among different facial expression
recognition (FER) datasets due to subjective annotating process and different
collecting conditions. Recent works resort to adversarial mechanisms that learn
domain-invariant features to mitigate domain shift. However, most of these
works focus on holistic feature adaptation, and they ignore local features that
are more transferable across different datasets. Moreover, local features carry
more detailed and discriminative content for expression recognition, and thus
integrating local features may enable fine-grained adaptation. In this work, we
propose a novel Adversarial Graph Representation Adaptation (AGRA) framework
that unifies graph representation propagation with adversarial learning for
cross-domain holistic-local feature co-adaptation. To achieve this, we first
build a graph to correlate holistic and local regions within each domain and
another graph to correlate these regions across different domains. Then, we
learn the per-class statistical distribution of each domain and extract
holistic-local features from the input image to initialize the corresponding
graph nodes. Finally, we introduce two stacked graph convolution networks to
propagate holistic-local feature within each domain to explore their
interaction and across different domains for holistic-local feature
co-adaptation. In this way, the AGRA framework can adaptively learn
fine-grained domain-invariant features and thus facilitate cross-domain
expression recognition. We conduct extensive and fair experiments on several
popular benchmarks and show that the proposed AGRA framework achieves superior
performance over previous state-of-the-art methods.

本文提出了一种名为 AGRA 的新颖的敌对图表示适应框架，将图表示传播与敌对学习结合起来，实现跨域整体 - 局部特征共适应，并探索它们的相互作用，使 AGRA 框架能够自适应地学习细粒度的不变特征，从而促进跨域表情识别。

面部表情跨领域识别的对抗性图表示适应

Adversarial Graph Representation Adaptation for Cross-Domain Facial  Expression Recognition

Data inconsistency leads to a slow training process when deep neural networks
are used for the inverse design of photonic devices, an issue that arises from
the fundamental property of non-uniqueness in all inverse scattering problems.
Here we show that by combining forward modeling and inverse design in a tandem
architecture, one can overcome this fundamental issue, allowing deep neural
networks to be effectively trained by data sets that contain non-unique
electromagnetic scattering instances. This paves the way for using deep neural
networks to design complex photonic structures that requires large training
sets.

通过在串联架构中结合正向建模和反向设计，可以有效地使用数据集对包含非唯一电磁散射实例的深度神经网络进行训练，为使用深度神经网络设计需要大型训练集的复杂光子结构铺平了道路。