Recently, there has been widespread interest in arbitrary-shaped scene text spotting, showing notable progress in both speed and accuracy. However, common issues such as model complexity, slow inference speed, and challenges in deployment persist. For this reason, this paper proposes Weakly Supervised Point-collection Network (WSPNet) for arbitrarily shaped scene text, which adopts deformable convolutional single-shot network architecture to realize multi-branch parallel reading of text, and combines weakly supervised text line point sampling to realize sampling recognition and transcription of arbitrarily shaped text instances. To enhance the robustness of multi-scale fusion, we utilize the Text Feature Fusion (TFF) module to dynamically acquire key features at different scales during the fusion process. This approach aims to reduce discrepancies in scale fusion. Extensive experiments on arbitrarily shaped benchmarks show that WSPNet achieves competitive accuracy and speed. For example, the proposed method achieves an end-to-end text recognition F-measure of 63.4 on the Total-Text dataset at 35.5 FPS, which is competitive with the best results in terms of speed and accuracy balance.
Dimensionality reduction techniques can remove redundant information from hyperspectral images (HSIs) and improve discriminability. However, due to the inherent nonlinear characteristics of HSI, there may be non-Euclidean structures in the data and its topological properties may make it suboptimal to recover the low-dimensional manifolds by means of a linear projection. As a result, linear projection from high-dimensional space to low-dimensional discriminative space is not always effective. To better explore the intrinsic geometric structure of HSI, we propose a Gaussian manifold metric learning (GMML) method, which performs explicit and nonlinear dimensionality reduction for HSI. It models pixel neighborhoods as Gaussian distributions to combine spatial and spectral information. In GMML, samples are mapped as elements on the Gaussian manifold consisting of Gaussian distributions to retain the intrinsic characteristics of data. By treating the inner product of the tangent space on the manifold as a kernel function, our method performs metric learning in the tangent space. Therefore, the proposed GMML method makes intra-class samples more compact and inter-class samples more separated while preserving the geometric structure information inherent in the data. Experiments on six real hyperspectral datasets validate the effectiveness of the proposed GMML algorithm to extract discriminating features compared to several related methods.
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