Search

This paper proposed an unsupervised domain adaptation person re-identification base on reliability integration (UDA-RI) method aimed at alleviating the negative impact of noisy labels in the pseudo-labeling-based unsupervised domain adaptation person re-identification (UDA person ReID). This method consisted of two parts, such as progressive pseudo label refinement strategy and reliability integration strategy. The progressive pseudo label refinement strategy established a quantitative standard for measuring the uncertainty of pseudo labels and adopted gradual sampling to make the model more stable during training. The reliability integration strategy considered knowledge from different adaptation moments, allocated weights according to the reliability levels of models from different iterations, integrated the self-integrated models with different architectures, and used them as the final inference model. Experimental results show that compared with the advanced unsupervised domain adaptation person re-identification methods, the UDA-RI method achieves superior performance on Market1501, Duke- MTMC-ReID, and MSMT17 datasets. [ABSTRACT FROM AUTHOR]

To address the issue that existing video colorization methods are complicated to guarantee both coloring quality and temporal consistency, this paper proposed a video colorization method AMVC-GAN combining attention mechanism and multi-scale feature fusion. Firstly, it proposed a GAN-based video colorization network model. It designed a multi-scale feature fusion module in the generator of GAN with a cyclic time network as the main body to obtain information of different time frequencies. Secondly, to effectively consider the relationship between adjacent frames, it used the features extracted from different time frequencies to strengthen the connection between frames as a way to enhance the temporal consistency of colorization. Finally, to obtain more helpful information, it introduced an attention module in the upsampling part, and optimally trained the results by utilizing PatchGAN to enhance the final colorization effect. Comparing with the state-of-the-art automatic video colorization methods on DAVIS and VIDEVO datasets, the results show that AMVC-GAN ranks first in multiple indicators, with better time consistency and colorization effect. Compared with other methods, AMVC-GAN can effectively reduce time flicker, while ensuring more real and natural colorization effect. [ABSTRACT FROM AUTHOR]

Video distortion mainly comes from the degradation of video quality caused by spatial and temporal distortion. Aiming at these two kinds of video quality degradation, this paper proposed a full-reference video quality assessment method STPFVQA combining spatio-temporal features and visual perception. Firstly, the method extracted spatial perceptual features from reference video and distorted video using ResNet50 convolutional network. Secondly, the method feeded the extracted spatial perceptual features into transformer’s encoder and decoder to construct the serialization relationship of the video, and at the same time to explore the effect of distortion on the video serialization relationship by comparing the reference video and distorted video Then, the method sent the output of the transformer to the prediction header to form a frame-level score Finally, in order to simulate the hysteresis of human visual system perception, this paper obtained the video quality score from the short-term, long-term and global memory effects. To verify the feasibility of the method, this paper conducted experiments on four datasets, respectively LIVE,IVC-IC,CSIQ,and IVPL. The experimental results show that the proposed method is more consistent with the perception of the human visual system. Compared with the state-of-the-art serial dependence model（SDM） on the IVC-IC and CSIQ datasets, the SROCC is 2.6% and 3.1% higher, the KROCC is 6.1% and 7.9% higher, and the PLCC is 2.3% and 5.5% higher. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)