Synthesizing high-quality photorealistic images with textual descriptions as
a condition is very challenging. Generative Adversarial Networks (GANs), the
classical model for this task, frequently suffer from low consistency between
image and text descriptions and insufficient richness in synthesized images.
Recently, conditional affine transformations (CAT), such as conditional batch
normalization and instance normalization, have been applied to different layers
of GAN to control content synthesis in images. CAT is a multi-layer perceptron
that independently predicts data based on batch statistics between neighboring
layers, with global textual information unavailable to other layers. To address
this issue, we first model CAT and a recurrent neural network (RAT) to ensure
that different layers can access global information. We then introduce shuffle
attention between RAT to mitigate the characteristic of information forgetting
in recurrent neural networks. Moreover, both our generator and discriminator
utilize the powerful pre-trained model, Clip, which has been extensively
employed for establishing associations between text and images through the
learning of multimodal representations in latent space. The discriminator
utilizes CLIP's ability to comprehend complex scenes to accurately assess the
quality of the generated images. Extensive experiments have been conducted on
the CUB, Oxford, and CelebA-tiny datasets to demonstrate the superiority of the
proposed model over current state-of-the-art models. The code is
this https URL

通过使用条件仿射变换以及在循环神经网络之间引入随机注意力机制，结合强大的预训练模型 Clip，本研究提出了一种高质量逼真图像的生成模型，有效提升了图像与文本描述之间的一致性和综合图像的丰富度。

RATLIP: 基于递归仿射变换的生成对抗网络 CLIP 文本到图像合成

RATLIP: Generative Adversarial CLIP Text-to-Image Synthesis Based on  Recurrent Affine Transformations

We introduce ReConvNet, a recurrent convolutional architecture for
semi-supervised video object segmentation that is able to fast adapt its
features to focus on any specific object of interest at inference time.
Generalization to new objects never observed during training is known to be a
hard task for supervised approaches that would need to be retrained. To tackle
this problem, we propose a more efficient solution that learns spatio-temporal
features self-adapting to the object of interest via conditional affine
transformations. This approach is simple, can be trained end-to-end and does
not necessarily require extra training steps at inference time. Our method
shows competitive results on DAVIS2016 with respect to state-of-the art
approaches that use online fine-tuning, and outperforms them on DAVIS2017.
ReConvNet shows also promising results on the DAVIS-Challenge 2018 winning the
$10$-th position.

ReConvNet 是一种循环卷积架构，用于半监督视频对象分割，能够快速适应其特征以聚焦于感兴趣的任何特定对象。通过条件仿射变换学习自适应于感兴趣的对象的时空特征的方法，不需要额外的推断时间内的训练步骤，且在 DAVIS2016 上表现出有竞争力的结果。