TMFN: a text-based multimodal fusion network with multi-scale feature extraction and unsupervised contrastive learning for multimodal sentiment analysis by Junsong Fu, Youjia Fu, Huixia Xue, Zihao Xu

“…Experimental results show that our proposed model outperforms the state-of-the-art models in MSA on two benchmark datasets.…”
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STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images by Wei Wu, Yapeng Liu, Kun Li, Haiping Yang, Liao Yang, Zuohui Chen

“…Experimental results demonstrate that STFCropNet outperforms state-of-the-art models in both study areas. STFCropNet achieves an overall accuracy of 83.2% and 90.6%, representing improvements of 3.6% and 4.1%, respectively, compared to the second-best baseline model. …”
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Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China by Pan Xiong, Dulin Zhai, Cheng Long, Huiyu Zhou, Xuemin Zhang, Xuhui Shen

“…These observations confirm that the proposed model outperforms several state‐of‐the‐art models in making predictions at different times and under diverse conditions.…”
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Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment by Praveen Vijai, Bagavathi Sivakumar P

“…These results significantly outperform state-of-the-art models, including traditional VAE, LSTM, and transformer-based approaches. …”
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G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images by Seungyoo Lee, Kyujin Han, Hangyeul Shin, Harin Park, Seunghyon Kim, Jeonghun Kim, Xiaopeng Yang, Jae Do Yang, Hee Chul Yu, Heecheon You

“…The proposed method outperformed the other state-of-the-art models on the three datasets, which demonstrated the strong effectiveness, robustness, and generalizability of the proposed method in liver segmentation.…”
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MultiChem: predicting chemical properties using multi-view graph attention network by Heesang Moon, Mina Rho

“…Our model achieved an average area under the receiver operating characteristic (AUROC) of 0.822 and a root mean squared error (RMSE) of 1.133, representing a 3% improvement in AUROC and a 7% improvement in RMSE over state-of-the-art models in extensive seed testing. Conclusion MultiChem highlights the importance of integrating both local and global structural information in predicting molecular properties, while also assessing the stability of the models across multiple datasets using various random seed values. …”
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DualCFGL: dual-channel fusion global and local features for sequential recommendation by Shuxu Chen, Yuanyuan Liu, Chao Che, Ziqi Wei, Zhaoqian Zhong

“…We conduct extensive experiments on three widely used datasets, and the results show that our model outperforms current state-of-the-art models.…”
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TraxVBF: A hybrid transformer-xLSTM framework for EMG signal processing and assistive technology development in rehabilitation by Seyyed Ali Zendehbad, Athena Sharifi Razavi, Marzieh Allami Sanjani, Zahra Sedaghat, Saleh Lashkari

“…For healthy participants, TraxVBF-type Base outperforms state of the art models (LSTM and GRU) with an MSE of 0.06 and R2 of 0.89. …”
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A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification by Ziwei Li, Weiming Xu, Shiyu Yang, Juan Wang, Hua Su, Zhanchao Huang, Sheng Wu

“…HGTNet achieves classification accuracies of 98.47%, 95.75%, and 96.33% on the aerial image, NWPU-RESISC45, and OPTIMAL-31 datasets, respectively, demonstrating superior performance compared to other state-of-the-art models. Extensive experimental results indicate that our proposed method effectively learns critical multiscale visual features and distinguishes between similar complex scenes, thereby significantly enhancing the accuracy of RSSC.…”
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Efficient ear alignment using a two‐stack hourglass network by Anja Hrovatič, Peter Peer, Vitomir Štruc, Žiga Emeršič

“…The authors evaluate the proposed framework in comprehensive experiments on the AWEx and ITWE datasets and show that the 2‐SHGNet model leads to more accurate landmark predictions than competing state‐of‐the‐art models from the literature. Furthermore, the authors also demonstrate that the alignment step significantly improves recognition accuracy with ear images from unconstrained environments compared to unaligned imagery.…”
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Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis by Ioannis Stathopoulos, Roman Stoklasa, Maria Anthi Kouri, Georgios Velonakis, Efstratios Karavasilis, Efstathios Efstathopoulos, Luigi Serio

“…While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts’ accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. …”
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Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification by Masoud Muhammed Hassan, Halbast Rashid Ismail

“…Experimental findings demonstrated that the proposed models surpassed other state-of-the-art models, achieving a test accuracy of 94.70 % and 77.00 % for CNN, 94.00 % and 86.00 % for BCNN-VI, and 93.30 % and 81.00 % for BCNN-MC-dropout on the APTOS dataset and Messidor-2 dataset, respectively. …”
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LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. …”
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Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

“…Using Shenzhen city as a testbed, extensive experimental results show that our approach improves accuracy by 13.3% compared to state-of-the-art models. We further validate the superior generalizability of our approach across various urban tasks, such as predicting urban land-use, housing prices, and population density, over other baselines. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…With a 55.18 Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, and a 63.6 BiLingual Evaluation Understudy 1 (BLEU1) score, our proposed model not only outperforms state-of-the-art models on the Phoenix14T dataset but also outperforms some of the best alternative architectures, specifically Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). …”
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NavBLIP: a visual-language model for enhancing unmanned aerial vehicles navigation and object detection by Ye Li, Li Yang, Meifang Yang, Fei Yan, Tonghua Liu, Chensi Guo, Rufeng Chen

“…Furthermore, NavBLIP employs a multimodal control strategy that dynamically selects context-specific features to optimize real-time performance, ensuring efficiency in high-stakes operations.Results and discussionExtensive experiments on benchmark datasets such as RefCOCO, CC12M, and Openlmages reveal that NavBLIP outperforms existing state-of-the-art models in terms of accuracy, recall, and computational efficiency. …”
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Improving Performance of Real-Time Object Detection in Edge Device Through Concurrent Multi-Frame Processing by Seunghwan Kim, Changjong Kim, Sunggon Kim

“…Additionally, it demonstrates improvements of <inline-formula> <tex-math notation="LaTeX">$2.10\times $ </tex-math></inline-formula> over state-of-the-art model optimization.…”
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Audio-visual event localization with dual temporal-aware scene understanding and image-text knowledge bridging by Pufen Zhang, Jiaxiang Wang, Meng Wan, Song Zhang, Jie Jing, Lianhong Ding, Peng Shi

“…Extensive experimental results demonstrate that DTKB significantly outperforms the state-of-the-arts models.…”
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Attention-based interactive multi-level feature fusion for named entity recognition by Yiwu Xu, Yun Chen

“…We conducted generous comparative experiments on three datasets, and the experimental results showed that our model achieved better performance than several state-of-the-art models.…”
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Transformer-Based Multi-Player Tracking and Skill Recognition Framework for Volleyball Analytics by Lei Jiang, Zhihong Yang, Lei Gang

“…Results demonstrate that our model achieves superior performance, with an IDF1 score of 72.6, MOTA of 94.8, and HOTA of 73.7, outperforming state-of-the-art models such as TransTrack and ByteTrack on the SportsMOT dataset. …”
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