Zero-shot learning (ZSL) is a challenging task due to the lack of unseen class data during training. Existing works attempt to establish a mapping between the visual and class spaces through a common intermediate semantic space. The main limitation of existing methods is the strong bias towards seen class, known as the domain shift problem, which leads to unsatisfactory performance in both conventional and generalized ZSL tasks. To tackle this challenge, we propose to convert ZSL to the conventional supervised learning by generating features for unseen classes. To this end, a joint generative model that couples variational autoencoder (VAE) and generative adversarial network (GAN), called Zero-VAE-GAN, is proposed to generate high-quality unseen features. To enhance the class-level discriminability, an adversarial categorization network is incorporated into the joint framework. Besides, we propose two self-training strategies to augment unlabeled unseen features for the transductive extension of our model, addressing the domain shift problem to a large extent. Experimental results on five standard benchmarks and a large-scale dataset demonstrate the superiority of our generative model over the state-of-the-art methods for conventional, especially generalized ZSL tasks. Moreover, the further improvement of the transductive setting demonstrates the effectiveness of the proposed self-training strategies.

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://github.com/Sonettoo/CRS-Diff.

The complex entanglement between darkness and noise hinders the advance of low-light image enhancement. Most existing methods adopted lightening-then-denoising or embedded a special denoising module into enhancement network without specific noise knowledge as supervision to restore low-light images. However, they either fail to remove the amplified noise or blur the detail information. Against above drawbacks, we propose a novel dual prior guidance method for low-light image enhancement that relights darkness and suppresses noise simultaneously. Concretely, the main novelties of our proposed method are three-fold. Firstly, our formulation originates from a statistic observation that darkness can be disentangled into luminance channel, yet noise still exists each channel when low-light images are transformed from RGB space to YCbCr space. It inspires us to design an ingenious method, extracting noise and darkness, termed END, to enhance low-light images. Secondly, we propose a prior extraction network with prior composition module to extract luminance and noise priors from different channels. Thirdly, an image enhancement network deployed with prior guidance module is proposed to progressively lighten the darkness and remove noise. Extensive experiments on multiple benchmarks demonstrate that our proposed method achieves remarkable performance compared to other state-of-the-art low-light image enhancement methods. The source code and trained model can be found in https://github.com/WHK-Huake/END.

Retinex theory-based low-light image enhancement methods have received increasing attention and achieved tremendous advancements. However, there still exist two seldom-explored issues: 1) The above methods only formally simulate the Retinex decomposition, resulting in lacking explicit interpretability. 2) They usually are performed in single-scale space, leading to suboptimal enhancement results. In this paper, we propose an interpretable Multi-scale Retinex Unfolding Network (MRUNet) for low-light image enhancement, which can tackle both of the aforementioned issues simultaneously. Specifically, we formulate low-light image enhancement as a multi-scale Retinex optimization problem and design an iteration minimization solution to solve it. The optimization solution is further unfolded to fabricate MRUNet, which is empowered with clear physical significance and multi-scale prior knowledge in favor of image enhancement. However, it will aggravate model size and efficiency when exploiting multiple proximal mapping networks to extract multi-scale prior from multi-scale inputs. To surmount the issue, we propose a Scale-Aware Proximal mapping Module (SAPM), which efficiently collect multi-scale prior knowledge via the weight sharing strategy. In SAPM, we tailor a scale-aware transformer to model the specific scale-similarity among different scales. Extensive experiments manifest that MRUNet surpasses other Retinex-based low-light image enhancement methods on multiple benchmarks. Our code and pretrained model can be found in https://github.com/WHK-Huake/MRUNet.

To improve the performance of sketch-based image retrieval (SBIR) methods, most existing SBIR methods develop brand new SBIR methods. In fact, a re-ranking approach, which can refine the retrieval results of SBIR methods, is also beneficial. Inspired by this, in this paper, an SBIR re-ranking approach based on multi-clustering is proposed. In order to make the re-ranking approach invisible to users and adaptive to different types of image datasets, we made it an unsupervised method using blind feedback. Distinguished from the existing methods, this re-ranking approach uses the semantic information of three types of images: edge maps, object images (images with black background and natural images’ foreground objects) and natural images themselves. With the initial retrieval results of an SBIR method, our approach first does the clustering operation for three types of images. Then, we utilize the clustering results to generate a cluster score for each initial retrieval result. Finally, the cluster score is used to calculate the final retrieval scores for the initial retrieval results. The experiments on different SBIR datasets are conducted. Experimental results demonstrate that, by implementing our re-ranking approach, the retrieval accuracy of a variety of SBIR methods is increased. Furthermore, the comparisons between our re-ranking method and the existing re-ranking methods are given.