Binfen Ding
Binfen Ding has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Jieyu An
School of Information Engineering, Gandong University, Fuzhou 344000, China
2 shared publications - Shaowen Liu
School of Information Engineering, Gandong University, Fuzhou 344000, China
1 shared publication - Yumeng Lei
School of Applied Engineering, Gandong University, Fuzhou 344000, China
1 shared publication
Latest Publications
- Open Access
- 16 September 2026
- Published by MDPI AG in Electronics
- Vol. 15 (18) , 4221
Recovery-Aware Learning for Remote Photoplethysmography During Post-Exercise Heart Rate Recovery
Remote photoplethysmography (rPPG) provides a contactless way to estimate cardiac activity from facial videos, but post-exercise recovery remains difficult because the heart rate changes rapidly and motion artifacts are often pronounced. This work focuses on rPPG-based heart rate estimation under these non-stationary conditions. We introduce a Recovery-Aware Learning framework in which a subject-specific recovery time is used only during training to guide window sampling and auxiliary supervision. Early recovery windows are sampled more frequently to reduce the imbalance between the short rapid-transition phase and the longer stable phase, while a soft recovery-progress target provides additional temporal guidance. We further propose ResPulseNet, a 1D convolutional encoder–decoder network designed to reconstruct rPPG waveforms from multi-ROI RGB traces while sharing temporal representations with the auxiliary recovery-progress task. On the MCD-rPPG dataset, ResPulseNet achieves an overall MAE of bpm and an MAE of bpm on post-exercise recordings, compared with 4.89 and 5.41 bpm, respectively, for the strongest evaluated baseline. These results show that combining recovery-aware training with dedicated temporal modeling improves rPPG estimation during post-exercise physiological transitions.- 16 September 2026
- Published by MDPI AG in Electronics
- Vol. 15 (18) , 4221
Open AccessRecovery-Aware Learning for Remote Photoplethysmography During Post-Exercise Heart Rate Recovery
Remote photoplethysmography (rPPG) provides a contactless way to estimate cardiac activity from facial videos, but post-exercise recovery remains difficult because the heart rate changes rapidly and motion artifacts are often pronounced. This work focuses on rPPG-based heart rate estimation under these non-stationary conditions. We introduce a Recovery-Aware Learning framework in which a subject-specific recovery time is used only during training to guide window sampling and auxiliary supervision. Early recovery windows are sampled more frequently to reduce the imbalance between the short rapid-transition phase and the longer stable phase, while a soft recovery-progress target provides additional temporal guidance. We further propose ResPulseNet, a 1D convolutional encoder–decoder network designed to reconstruct rPPG waveforms from multi-ROI RGB traces while sharing temporal representations with the auxiliary recovery-progress task. On the MCD-rPPG dataset, ResPulseNet achieves an overall MAE of bpm and an MAE of bpm on post-exercise recordings, compared with 4.89 and 5.41 bpm, respectively, for the strongest evaluated baseline. These results show that combining recovery-aware training with dedicated temporal modeling improves rPPG estimation during post-exercise physiological transitions. - Open Access
Learning Complementary Representations for Targeted Multimodal Sentiment Analysis
Targeted multimodal sentiment classification is frequently impeded by the semantic sparsity of social media content, where text is brief and context is implicit. Traditional methods that rely on direct concatenation of textual and visual features often fail to resolve the ambiguity of specific targets due to a lack of alignment between modalities. In this paper, we propose the Complementary Description Network (CDNet) to bridge this informational gap. CDNet incorporates automatically generated image descriptions as an additional semantic bridge, in contrast to methods that handle text and images as distinct streams. The framework enhances the input representation by directly translating visual content into text, allowing for more accurate interactions between the opinion target and the visual narrative. We further introduce a complementary reconstruction module that functions as a regularizer, forcing the model to retain deep semantic cues during fusion. Empirical results on the Twitter-2015 and Twitter-2017 benchmarks confirm that CDNet outperforms existing baselines. The findings suggest that visual-to-text augmentation is an effective strategy for compensating for the limited context inherent in short texts.
Open AccessLearning Complementary Representations for Targeted Multimodal Sentiment Analysis
Targeted multimodal sentiment classification is frequently impeded by the semantic sparsity of social media content, where text is brief and context is implicit. Traditional methods that rely on direct concatenation of textual and visual features often fail to resolve the ambiguity of specific targets due to a lack of alignment between modalities. In this paper, we propose the Complementary Description Network (CDNet) to bridge this informational gap. CDNet incorporates automatically generated image descriptions as an additional semantic bridge, in contrast to methods that handle text and images as distinct streams. The framework enhances the input representation by directly translating visual content into text, allowing for more accurate interactions between the opinion target and the visual narrative. We further introduce a complementary reconstruction module that functions as a regularizer, forcing the model to retain deep semantic cues during fusion. Empirical results on the Twitter-2015 and Twitter-2017 benchmarks confirm that CDNet outperforms existing baselines. The findings suggest that visual-to-text augmentation is an effective strategy for compensating for the limited context inherent in short texts.