RodEpil: A Video Dataset of Laboratory Rodents for Seizure Detection and Benchmark Evaluation

We introduce a curated video dataset of laboratory rodents for automatic detection of convulsive events. The dataset contains short (10~s) top-down and side-view video clips of individual rodents, labeled at clip level as normal activity or seizure. It includes 10,101 negative samples and 2,952 positive samples collected from 19 subjects. We describe the data curation, annotation protocol and preprocessing pipeline, and report baseline experiments using a transformer-based video classifier (TimeSformer). Experiments employ five-fold cross-validation with strict subject-wise partitioning to prevent data leakage (no subject appears in more than one fold). Results show that the TimeSformer architecture enables discrimination between seizure and normal activity with an average F1-score of 97%. The dataset and baseline code are publicly released to support reproducible research on non-invasive, video-based monitoring in preclinical epilepsy research. RodEpil Dataset access - DOI: 10.5281/zenodo.17601357

A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion

With the advancement of artificial intelligence and computer vision technologies, multimodal emotion recognition has become a prominent research topic. However, existing methods face challenges such as heterogeneous data fusion and the effective utilization of modality correlations. This paper proposes a novel multimodal emotion recognition approach, DeepMSI-MER, based on the integration of contrastive learning and visual sequence compression. The proposed method enhances cross-modal feature fusion through contrastive learning and reduces redundancy in the visual modality by leveraging visual sequence compression. Experimental results on two public datasets, IEMOCAP and MELD, demonstrate that DeepMSI-MER significantly improves the accuracy and robustness of emotion recognition, validating the effectiveness of multimodal feature fusion and the proposed approach.