This paper proposes the Incremental Information-Aware (IIA) framework to enhance video captioning by generating semantically abundant and accurate descriptions. The Semantic Incremental Information Aware model (Semantic-IIA) aims to capture detailed semantic information, while the Structural Incremental Information Aware model (Structural-IIA) focuses on identifying key structural content. These models are designed in parallel to complement each other, ensuring both semantic abundance and structural accuracy in generated captions. Experiments on the MSVD and MSR-VTT datasets demonstrate superior performance, with CIDEr scores of 108.4 and 59.8, and BLEU-4 scores of 61.0 and 47.3, respectively. Our code is available at https://github.com/Zhongnibug/IIA.

This paper presents a new spectral clustering method called correlation preserving indexing (CPI), which is performed in the correlation similarity measure space. In this framework, the documents are projected into a low-dimensional semantic space in which the correlations between the documents in the local patches are maximized while the correlations between the documents outside these patches are minimized simultaneously. Since the intrinsic geometrical structure of the document space is often embedded in the similarities between the documents, correlation as a similarity measure is more suitable for detecting the intrinsic geometrical structure of the document space than euclidean distance. Consequently, the proposed CPI method can effectively discover the intrinsic structures embedded in high-dimensional document space. The effectiveness of the new method is demonstrated by extensive experiments conducted on various data sets and by comparison with existing document clustering methods.

Handwriting-based writer identification is a hot research topic in the pattern recognition field. Nowadays, online handwriting-based writer identification is steadily growing toward its maturity. On the contrary, offline handwriting-based writer identification still remains as a challenging problem because writing features only can be extracted from the handwriting image in this situation. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is lost. At present, 2D Gabor filter method is widely acknowledged as a good method for offline handwriting identification, however it still suffers from some inherent disadvantages, such as the high computational cost. In this paper, we present a novel wavelet-based GGD method to replace the traditional 2D Gabor filters. Shown in our experiments, this novel method not only achieves better experiment results but also greatly reduces the elapsed time on calculation.

Proposes a method to tackle the problem of detecting skilled forgeries in off-line signature verification. Inspired by the approach adopted by expert examiners, it is based on a smoothness criterion. From a collection of genuine and forged signatures, it is observed that, although skilled forgery signatures are very similar to genuine ones on a global scale, they are generally less smooth and natural on a detailed scale than the genuine ones, especially for those skilled forgery signatures which consist of cursive graphic patterns. A smoothness index is derived from such signatures. This is combined with other global shape features and used for verification. Satisfactory results are obtained.