Owing to powerful generative priors, Text-to-Image (T2I) diffusion models have achieved promising results in image colorization task. However, recent advanced methods primarily integrate global semantics. Such practice neglects local semantics, yielding suboptimal colorization performance. In this paper, we present a novel global-local semantic aware colorization method named GoLoColor, which performs semantic awareness at both global and local levels. The GoLoColor includes Global Aware (GoA) module, Local Aware module (LoA) and Semantic Aggregation (SA) module for semantic understanding. Specifically, the GoA produces global semantic embedding to represent whole image, while the LoA provides semantic support for local objects, particularly in scenes containing multiple entities. The SA module facilitates semantic interaction between local and global semantic embedding to produce richer semantic information. Finally, a controlled T2I diffusion model is utilized to produce color image guided by the aggregated semantic embedding. Comprehensive experiments demonstrate that our method achieves superior performance and can produce realistic colorization.

In the face of high-dimensional and complex data, effective subspace can preserve specific statistical properties and provide an appropriate representation of data, which generally facilitates the underlying tasks such as clustering or classification. Meanwhile, multiple kernel learning is a technique to combine multiple kernels from different feature spaces effectively. Thus, by incorporating multiple kernels into the process of subspace learning, different feature spaces can be projected into a unified subspace. This paper proposes the Multiple Kernel Subspace Learning (MKSL) for embedding the original space into a unified subspace. Multiple kernels of different feature spaces are combined by MKSL in the process of learning, which can extend the suitability for various applications. Moreover, to generate the optimal combination kernel of subspace learning, we propose a two-step iteration strategy to learn the appropriate kernel weights and transformation matrix of projecting simultaneously. Furthermore, our proposed formulation of MKSL can introduce different prior knowledge such as class information and neighborhood relationships. Thus it is competent to the unsupervised learning, semi-supervised learning, and supervised learning. Extensive experiments are conducted on diverse datasets, and the performances are comprehensively evaluated on different tasks. The experimental results indicate that the proposed algorithm is outstanding in unsupervised clustering task and effective in supervised and semi-supervised classification tasks.

In multilabel images, the changeable size, posture, and position of objects in the image will increase the difficulty of classification. Moreover, a large amount of irrelevant information interferes with the recognition of objects. Therefore, how to remove irrelevant information from the image to improve the performance of label recognition is an important problem. In this article, we propose a convolutional network based on feature denoising and details supplement (FDDS) to address this issue. In FDDS, we first design a cascade convolution module (CCM) to collect spatial details of upper features, in order to enhance the information expression of features. Second, the feature denoising module (FDM) is further put forward to reallocate the weight of the feature semantic area, in order to enrich the effective semantic information of the current feature and perform denoising operations on object-irrelevant information. Experimental results show that the proposed FDDS outperforms the existing state-of-the-art models on several benchmark datasets, especially for complex scenes.

The multi-label image classification problem is one of the most important problems in the field of computer vision, which needs to predict and output all the labels in an image. Multiple labels to be classified in an image increases the difficulty of image classification, and multi-label image classification usually requires additional attention to the positions of the object with different scales and poses. Hence, how to use the dependency relationship between labels to improve the recognition accuracy is an important problem when the object is difficult to directly identify. In this paper, we propose a designed network called the Semantic Supplementary Network with Prior Information (SSNP) to address this problem. The proposed SSNP first generates prior information by using a prior information network with different convolutional layers. Then the semantic supplementary module generates semantic information of the potential labels that is highly relevant to the current information based on the prior information, thereby effectively using the dependency relationship between the labels to improve the classification accuracy. Different from existing methods which pay more attention to the image feature extraction process, we focus on the impact of high-level semantic information generated after feature extraction on the results and tap the potential of high-level semantic information through a semantic supplementary module to strengthen the potential dependence between labels. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves the state-of-the-art performance, especially when predicting some semantically dependent labels.

Estimating unknown wall parameters is of great importance for the application of through-the-wall radar (TWR). The time-delay-only estimation (TDOE) method is able to efficiently retrieve the constitute parameters of the homogeneous wall under test. For the TDOE method, the estimation accuracy of time delays associated with wall reflections directly affects the accuracy of wall parameters estimation. In this paper, we propose a sparse recovery method for estimation of wall parameters, which utilizes the orthogonal matching pursuit (OMP) algorithm to perform the time delay estimation of echoes backscattered from the wall at each antenna separation. Numerical simulation results have shown that the proposed estimation method is capable of providing the estimation of wall parameters with higher accuracy.