Epilepsy is a prevalent neurological disorder with significant detrimental effects on health. Accurate seizure detection is crucial for the precise diagnosis and effective treatment of epilepsy. Brain signals is widely recognized as a reliable clinical tool for diagnosing and evaluating severity of seizures. Traditionally, medical researchers have relied on visual inspection to identify and locate seizures and epileptogenic areas. However, manual analysis of brain data is both subjective and time-consuming. In recent years, there has been a surge in studies focusing on automatic seizure detection algorithms based on brain signals, driven by the advancements in artificial intelligence and digital brain signal technology. Nevertheless, in tackling this task, many of these studies have neglected to leverage the rich implicit information of samples to extract comprehensive feature representation for enhancing model performance. To address this gap, we propose a generalized model called SyncLearnNet for seizure detection based on brain signals. SyncLearnNet incorporates VariaScan and BatchAttention modules designed to fully utilize both intra-sample and inter-sample information, thereby improving feature discrimination without requiring additional data. Furthermore, the introduction of CurriClassifier aims to enhance the model's generalization performance. Experiments conducted on a public human seizure dataset CHB-MIT and a self-built animal seizure dataset comprising data from five rats demonstrated this method outperforms existing seizure detection methods in terms of generalization performance.

Discovering temporal boundary is critical for untrimmed video tasks, such as temporal sentence grounding and action detection. Due to the labor-intensive boundary annotations, the recent studies focus on the weakly-supervised setting, with only sentences or action tags in the training videos. However, how to align temporal boundaries and textual descriptions is problematic in most weakly-supervised approaches. To alleviate this difficulty, we propose a novel Dual Masked Modeling (DM2) framework, which can effectively enhance clip-text alignment to boost temporal boundary discovery, by cross-modal masked modeling in the dual fashion. Specifically, we introduce two coupled reconstruction branches, i.e., Clip-Aware Masked Text Modeling (C-MTM), and Text-Aware Masked Clip Modeling (T-MCM), after generating a temporal proposal of the underlying clip. In C-MTM, we recover the masked text with visual assistance of the clip proposal. In T-MCM, we recover the masked clip proposal with lingual assistance of the text. Via such complementary reconstruction supervision, our DM2 can cooperatively exploit robust matching between the video clip and the referred text, allowing to unify grounding and localization in a concise manner. Finally, we perform extensive experiments on the popular temporal benchmarks, i.e., Charades-STA, ActivityNet Captions, ActivityNet-v1.3 and THUMOS-14. Our DM2 achieves state-of-the-art for both weakly-supervised temporal grounding and localization. Codes and models will be released afterward.