Diffusion Models are a potent class of generative models capable of producing high-quality images. However, they often inadvertently favor certain data attributes, undermining the diversity of generated images. This issue is starkly apparent in skewed datasets like CelebA, where the initial dataset disproportionately favors females over males by 57.9%, this bias amplified in generated data where female representation outstrips males by 148%. In response, we propose a plug-and-play method named Manifold Guidance Sampling, which is also the first unsupervised method to mitigate bias issue in DDPMs. Leveraging the inherent structure of the data manifold, this method steers the sampling process towards a more uniform distribution, effectively dispersing the clustering of biased data. Without the need for modifying the existing model or additional training, it significantly mitigates data bias and enhances the quality and unbiasedness of the generated images.

In this paper, we introduce an Feature Aggregate Side Network (FASN), a simple, efficient, and easy-to-train method for open-vocabulary semantic segmentation. Building upon existing models based on the CLIP-Side Network framework, we address the issue of CLIP-generated features lacking pixel-level recognition capability by layering a novel fast graph representation learning layer between the CLIP and side networks. This integration introduces an inductive bias for the aggregation of local features, thereby better addressing the challenges in semantic segmentation. Through validation on five distinct datasets and extensive ablation studies, we have demonstrated the effectiveness of our modifications. Our findings indicate that with a slight increase in the number of parameters, there is a significant enhancement in performance.

While generative adversarial networks have made significant strides in natural image synthesis, their performance in specialized remote sensing (RS) imagery, particularly in capturing fine details of small objects like airplanes and ships, needs enhancement. This deficiency frequently results in shape distortion within the generated images. This challenge, compounded by the prohibitive costs of annotating RS images, motivates the development of an efficient unsupervised approach. In response, this paper introduces Object-Aided Generative Adversarial Network (OAGAN), an innovative model for unsupervised RS image generation. Initially, it employs an object-centric learning mechanism to extract structural semantic maps of foreground objects, without the need for labels. Subsequently, this paper proposes the Shape Encoding Layer (SEL) to encode the structural semantics of objects, which is seamlessly integrated into the intermediate layers of the generative model. This integration enables the model to prioritize the structural information of foreground objects. Additionally, to enhance the diversity of generated images, this paper designs a novel style regularization term. Comprehensive experiments are conducted on three distinct RS image datasets. Experiment results demonstrate that the proposed method surpasses state-of-the-art models in terms of the quality of generated images.

Generative adversarial networks (GANs) have shown notable accomplishments in remote sensing (RS) domain. However, this article reveals that their performance on RS images falls short when compared to their impressive results with natural images. This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting on RS images. To address this challenge, this article analyzes the characteristics of RS images and proposes manifold constraint regularization (MCR), a novel approach that tackles overfitting of GANs on RS images for the first time. Our method includes a new measure for evaluating the structure of the data manifold. Leveraging this measure, we propose the MCR term, which not only alleviates the overfitting problem, but also promotes alignment between the generated and real data manifolds, leading to enhanced quality in the generated images. The effectiveness and versatility of this method have been corroborated through extensive validation on various RS datasets and GAN models. The proposed method not only enhances the quality of the generated images, reflected in a 3.13% improvement in Fréchet inception distance (FID) score, but also boosts the performance of the GANs on downstream tasks, evidenced by a 3.76% increase in classification accuracy. The source code is available athttps://github.com/rootSue/Manifold-RSGAN.

Recent studies have shown that machine models do not coincide with the human perception of image quality, making mainstream image enhancement methods not always compatible with downstream tasks. To ameliorate this issue, this paper targets homography estimation, which is a fundamental step in image interpretation, to explore a hybrid-degraded image enhancement approach. Specifically, we build a reinforcement learning image processing framework with multiple lightweight tools, and develop a two-policy agent to progressively handle hybrid-degraded images. To match homography estimators with different properties, we further propose an environmental epistemic model (EEM) to build task-specific prior knowledge of uncertain environments. During the training process, the EEM is updated online and used to guide the agent’s exploration and exploitation. Comprehensive experiments are conducted on the aerial dataset where images are degraded from four visual perspectives: brightness, contrast, sharpness and stimulus. Results show that our agent can be trained to generate high-quality images for both learning-based and traditional homography estimators.

Abstract Compared with natural images, geospatial images cover larger area and have more complex image contents. There are few algorithms for generating controllable geospatial images, and their results are of low quality. In response to this problem, this paper proposes Geospatial Style Generative Adversarial Network to generate controllable and high‐quality geospatial images. Current conditional generators suffer the mode collapse problem in geospatial field. The problem is addressed via a modified mode seeking regularization term with contrastive learning theory. Besides, the discriminator network architecture is modified to process global feature information and texture information of geospatial images. Feature loss in the generator is introduced to stabilize the training process and improve generated image quality. Comprehensive experiments are conducted on UC Merced Land Use Dataset, NWPU‐RESISC45 Dataset, and AID Dataset to evaluate all compared methods. Experiment results show our method outperforms state‐of‐the‐art models. Our method not only generates high‐quality and controllable geospatial images, but also enhances the discriminator to learn better representations.