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Cross-modal pedestrian re-identification is a key technology to realize all-weather intelligent video surveillance system. This technology is designed to match the visible light image and infrared image of a pedestrian with a specific identity in a non-overlapping camera scene, so it faces huge intra-class changes and modal differences. Existing methods are difficult to solve these two difficulties, which is largely due to the lack of effective mining of feature discrimination and the full use of multi-source heterogeneous information. In view of the above shortcomings, a refined multi-source feature collaborative network is designed by the collaborative learning method, and multiple complementary features are extracted for information fusion, the learning ability of the network is improved. Multi-scale and multi-level features are extracted from the backbone convolutional network, and the refined feature collaborative learning is realized; the discriminative ability of features is enhanced to deal with intra-class changes. A modal sharing and unique feature collaboration module and a cross-modal human semantic self-supervision module are designed to achieve the purpose of multi-source feature collaborative learning, so as to improve the utilization of multi-source heterogeneous image information, and then modal differences are resolved. The validity and advancement of this method are verified on the SYSU-MM01 and RegDB data sets. [ABSTRACT FROM AUTHOR]

In order to develop a fast, efficient and intelligent tool to recognize anti-hypertensive peptides (AHTPs), an identification model based on multi-source characteristics and deep learning was constructed for the recognition of AHTPs. Novel enhanced grouped amino acid composition (NEGAAC), reduced dipeptide composition (RDPC), dipeptide deviation from expected mean (DDE), amino acid physicochemical properties-based distance transformation (AAP-DT) and BLOSUM62 encoding were used for feature extraction of peptide sequences. In addition, bidirectional gated recurrent units (BiGRU) were used for deep learning of protein characteristics, so as to effectively identify AHTPs. Under 10-fold cross-validation, the recognition accuracy of the recognition model based on multi-source features and deep learning reaches 96.78% and 98.72% on the benchmark data set and independent data set. [ABSTRACT FROM AUTHOR]