Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.

Weakly supervised anomalous behavior detection is a popular area at present. Compared to semi-supervised anomalous behavior detection, weakly-supervised learning both eliminates the need to crop videos and solves the problem of semi-supervised learning’s difficulty in handling long videos. Previous work has used graph convolution or self-attention mechanisms to model temporal relationships. However, these methods tend to model temporal relationships at a single scale and lack consideration of the aggregation problem for different temporal relationships. In this paper, we propose a weakly supervised anomaly detection framework, MTDA-Net, with emphasis on modeling different temporal relationships and enhanced semantic discrimination. To this end, we construct a new plug-and-play module, MTDA, which uses three branches, Multi-headed Attention (MHA), Temporal Shift (TS), and Dilated Aggregation (DA), to extract different temporal sequences. Specifically, the MHA branch can globally model the video information and project the features into different semantic spaces to enhance the expressiveness and discrimination of the features. The DA branch extracts temporal information of different scales via dilated convolution and captures the temporal features of local regions in the video. The TS branch can fuse the features of adjacent frames on a local scale and enhance the information flow. MTDA-Net can learn the temporal relationships between video segments on different branches and learn powerful video representations based on these relationships. The experimental results on the XD-Violence dataset show that MTDA-Net can significantly improve the detection accuracy of abnormal behaviors.

Regional rainfall-runoff modeling is a classic and significant research topic in hydrological sciences. Currently, the predominant modeling approach is developing data-driven models. This study proposes a rainfall-runoff model named ED-TimesNet (Encoder–Decoder-based TimesNet), which consists of convolutional neural networks. It transforms a one-dimensional time series into a two-dimensional matrix based on frequency-domain partitioning rules and subsequently employs a two-dimensional visual backbone to learn both local and global features of the hydrological time series. Compared to LSTM-based models and Transformer models, this model learns both intra-period and inter-period variations in hydrological series, simultaneously focusing on the relationships between adjacent and non-adjacent time points. It alleviates the temporal ambiguity problem inherent in attention mechanisms. This research validates the performance of the ED-TimesNet model in regional rainfall-runoff modeling tasks using the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset. The model achieves a median and mean NSE of 0.8049 and 0.7808, respectively, across 448 basins, outperforming the benchmark LSTM, VIC, and mHM models, and achieving comparable performance to the Transformer model. This paper does not address the model’s performance on ungauged basins. The method of predicting runoff based on the periodic features of hydrological data provides a novel perspective for hydrological sciences.

In the task of Facial Expression Recognition (FER), data uncertainty has been a critical factor affecting performance, typically arising from the ambiguity of facial expressions, low-quality images, and the subjectivity of annotators. Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training. To address this issue, we propose an uncertainty-based robust sample selection strategy, which combines confidence error with RandAugment to improve image diversity, effectively reducing overfitting caused by uncertain samples during deep learning model training. To validate the effectiveness of the proposed method, extensive experiments were conducted on FER public benchmarks. The accuracy obtained were 89.08% on RAF-DB, 63.12% on AffectNet, and 88.73% on FERPlus.

Faced with the surge of massive natural-language content, information retrieval systems must handle increasingly complex queries while filtering noisy information effectively. Although conventional approaches have made notable progress in matching efficiency and general adaptability, they still struggle to precisely model deep semantic associations between query intent and documents in real-world environments. Such limitations can lead to ranking deviations and omission of critical information. Motivated by recent advances in large language models (LLMs) and their capability to capture deep semantics, we propose DPR-FL, a Dual-Path Retrieval method that integrates fusion-based Filtering with structured LLM Feedback. It combines direct retrieval with a generation-guided retrieval process to form cooperative information flows. By fusing candidate results from multiple sources, applying a high-dimensional semantic filtering strategy, and leveraging LLM-based semantic feedback, DPR-FL refines and optimizes the selection of relevant documents, improving both coverage and relevance. Furthermore, the framework supports adaptive weighting of candidate sources and semantic signals, enhancing robustness in heterogeneous retrieval scenarios. Together, these components enable finer-grained information selection and semantic enrichment, substantially improving retrieval performance and result reliability in complex contexts. Experimental evaluations on standard web search benchmarks, including TREC DL’19 and DL’20, as well as low-resource BEIR datasets, demonstrate that DPR-FL achieves measurable gains across key metrics such as NDCG@10 and MAP, showing improved generalization, robustness, and adaptability in zero-shot retrieval settings.