Automatic perception of image quality is a challenging problem that impacts
billions of Internet and social media users daily. To advance research in this
field, we propose a no-reference image quality assessment (NR-IQA) method
termed Cross-IQA based on vision transformer(ViT) model. The proposed Cross-IQA
method can learn image quality features from unlabeled image data. We construct
the pretext task of synthesized image reconstruction to unsupervised extract
the image quality information based ViT block. The pretrained encoder of
Cross-IQA is used to fine-tune a linear regression model for score prediction.
Experimental results show that Cross-IQA can achieve state-of-the-art
performance in assessing the low-frequency degradation information (e.g., color
change, blurring, etc.) of images compared with the classical full-reference
IQA and NR-IQA under the same datasets.

提出了一种基于 Vision Transformer (ViT) 模型的无参考图像质量评估 (NR-IQA) 方法 Cross-IQA，可以从无标签图像数据中学习图像质量特征，并利用预训练的编码器进行线性回归模型的微调，实现了对图像的低频降级信息（如颜色变化、模糊等）的先进性能评估。

交叉 IQA: 无监督学习图像质量评估

Cross-IQA: Unsupervised Learning for Image Quality Assessment

Recent text-to-image models have achieved impressive results. However, since
they require large-scale datasets of text-image pairs, it is impractical to
train them on new domains where data is scarce or not labeled. In this work, we
propose using large-scale retrieval methods, in particular, efficient
k-Nearest-Neighbors (kNN), which offers novel capabilities: (1) training a
substantially small and efficient text-to-image diffusion model without any
text, (2) generating out-of-distribution images by simply swapping the
retrieval database at inference time, and (3) performing text-driven local
semantic manipulations while preserving object identity. To demonstrate the
robustness of our method, we apply our kNN approach on two state-of-the-art
diffusion backbones, and show results on several different datasets. As
evaluated by human studies and automatic metrics, our method achieves
state-of-the-art results compared to existing approaches that train
text-to-image generation models using images only (without paired text data)

通过使用大规模检索方法（尤其是高效的 k - 最近邻），我们提出了一种小型高效的文本到图像扩散模型，该模型不需要文本，并且可以在保持对象身份的同时执行基于文本驱动的局部语义操作。我们的方法在多个数据集上实现了最先进的结果，并且与使用仅图像而没有配对文本数据进行文本到图像生成模型训练的现有方法相比，在人类研究和自动指标方面都取得了最先进的结果。

KNN-Diffusion: 基于大规模检索的图像生成

KNN-Diffusion: Image Generation via Large-Scale Retrieval

While class activation map (CAM) generated by image classification network
has been widely used for weakly supervised object localization (WSOL) and
semantic segmentation (WSSS), such classifiers usually focus on discriminative
object regions. In this paper, we propose Contrastive learning for
Class-agnostic Activation Map (C$^2$AM) generation only using unlabeled image
data, without the involvement of image-level supervision. The core idea comes
from the observation that i) semantic information of foreground objects usually
differs from their backgrounds; ii) foreground objects with similar appearance
or background with similar color/texture have similar representations in the
feature space. We form the positive and negative pairs based on the above
relations and force the network to disentangle foreground and background with a
class-agnostic activation map using a novel contrastive loss. As the network is
guided to discriminate cross-image foreground-background, the class-agnostic
activation maps learned by our approach generate more complete object regions.
We successfully extracted from C$^2$AM class-agnostic object bounding boxes for
object localization and background cues to refine CAM generated by
classification network for semantic segmentation. Extensive experiments on
CUB-200-2011, ImageNet-1K, and PASCAL VOC2012 datasets show that both WSOL and
WSSS can benefit from the proposed C$^2$AM.

该研究提出了使用无标签图像数据进行不需要图像级监督的类无关激活图生成的对比学习方法（Contrastive learning for Class-agnostic Activation Map），得到了更完整的物体区域，并应用于弱监督目标定位和语义分割中。