Multimodal sentiment analysis (MSA) provides a novel way to understand human sentiments. However, the differences between distribution patterns across modalities bring challenges in this domain. The inconsistency of recognitions with different modalities leads to incorrect final results. Moreover, the gaps between sentiments with different degrees are small in one modality, but the gaps between sentiments with same degree are large across different modalities. The imbalance leads to incorrect recognition for different sentiment degrees. Since the fuzzy network shows excellent performance in integrating data from multiple modalities, this study constructs a fuzzy cognition-based dynamic fusion network (Fcdnet) for MSA. The Fcdnet dynamically integrates sentiment scores across different modalities using a fuzzy cognition fusion mechanism (FCM), significantly enhancing the accuracy of identifying divergent sentiments across modalities. Additionally, a disparity balancing module (DBM) is proposed to normalize the representations between different modality features by penalizing the similarity of sentiments with different degrees and rewarding the separability of sentiments with same degree. Experimental results demonstrate that Fcdnet outperforms state-of-the-art methods on public datasets, validating the superiority and effectiveness.

Action detection and understanding provide the foundation for the generation and interaction of multimedia content. However, existing methods mainly focus on constructing complex relational inference networks, overlooking the judgment of detection effectiveness. Moreover, these methods frequently generate detection results with cognitive abnormalities. To solve the above problems, this study proposes a cognitive effectiveness network based on fuzzy inference (Cefdet), which introduces the concept of 'cognition--based detection' to simulate human cognition. First, a fuzzy-driven cognitive effectiveness evaluation module (FCM) is established to introduce fuzzy inference into action detection. FCM is combined with human action features to simulate the cognition-based detection process, which clearly locates the position of frames with cognitive abnormalities. Then, a fuzzy cognitive update strategy (FCS) is proposed based on the FCM, which utilizes fuzzy logic to re-detect the cognition-based detection results and effectively update the results with cognitive abnormalities. Experimental results demonstrate that Cefdet exhibits superior performance against several mainstream algorithms on the public datasets, validating its effectiveness and superiority.

Reliability is an important property in the applied engineering systems, especially in visual tracking. The supervised visual tracking method uses reliable ground truth that is manually annotated, which is hard to get in many applications. However, weakly supervised visual trackings are limited by the low-quality labels. Therefore, a reliable sample selection strategy is the most important issue for the weakly supervised visual trackings. In this article, we propose an optimal sample selection strategy and apply it to the visual tracking system. The strategy first assesses the reliability of the samples according to the score map, where the score map is the pseudolabel generated by the upstream task to meet the needs of the downstream task. Then, the unreliable pseudolabels are replaced by reliable ground truth or discarded to overcome the degraded modeling problem by filtering low-quality samples. Finally, through comparison with multiple selection strategies, it is verified that the model trained using this strategy has the best performance. The proposed visual tracking model achieves the best performance among multiple assessment metrics in multiple datasets. Experiments verify that the scientific sample quality assessment method is very important. It can guide the improvement of model performance, which is of great help to the weakly supervised learning systems based on data.

Occlusion, rotation and other factors affect human motion structure because of the incomplete acquired image sequence, resulting in poor performance of non-rigid three-dimensional (3D) motion pose reconstruction. A non-rigid 3D reconstruction and high-precision correction method for motion pose are studied in this paper. A non-rigid imaging model is designed to obtain 3D moving images. According to the frame difference and morphological processing, the background of image is separated and denoised. Combined with motion analysis, 3D motion pose features are extracted as identification of non-rigid 3D motion error actions in a hybrid Convolution Neural Network-Hidden Markov Model to train the correction coefficients, which are used to adjust the pose in 3D motion reconstruction and realise correction. Experimental results show that this method has high precision reconstruction and correction of non-rigid 3D motion pose.

Action recognition in video is a research hot spot in the field of computer vision. Learning important clues in video context has significant effect to promote the interaction prediction and gesture recognition. Most existing methods infer the interactions between actor and context through relational reasoning methods. While these relational features contribute to improve the salience of action performance, the error will occur when the salient region is irrelevant to the recognized action. Therefore, this paper establishes a human-centered attention mechanism that dynamically highlights regions associated with action recognition according to target appearance to selectively recognize the human-object interaction action. The effectiveness of the proposed mechanism is verified on the AVA2.2 data set, and the visualized attention map further shows that the proposed attention mechanism can effectively recognize human-centered strongly correlated action.

Summary For avoiding the phenomenon of congestion, delay, and jitter in thedatabase access path, it is necessary to study the prediction method of database access path. Predicted database access path has high delay and jitter when using existing database access path prediction method to predict the path. Therefore, a prediction method of database access path is proposed based on the recurrent neural network. A decision matrix is constructed and normalized based on the analytic hierarchy process. The evaluation value of the alternative transit data center is calculated by the arithmetic weighted average operator, and the transit data center is selected according to the evaluation result. The matter‐element analysis model is used to establish the mapping relationship between the user experience quality and the network service quality parameters. Moreover, the user experience quality evaluation level objective function of the database access path prediction method is constructed. Through the objective function to obtain the optimal database access path, the database access path prediction is completed. The experimental results show that the delay of the database access path predicted by the proposed method is much lower than other methods. The jitter is less than 30 ms and the jitter is small, which verifies the effectiveness of the path prediction method.