# EXIST 2026: A Multimodal Sexism Detection System Integrating Eye-Tracking, Heart Rate, and EEG Signals > A People-Centered Multimodal Sexism Detection Study Combining Eye-Tracking, Heart Rate Monitoring, EEG, and Vision-Language Models - 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm) - 发布时间: 2026-06-06T09:27:48.000Z - 最近活动: 2026-06-06T09:57:28.499Z - 热度: 161.5 - 关键词: 多模态学习, 性别歧视检测, 眼动追踪, EEG, 心率监测, 内容审核, TikTok, 视觉语言模型, AI安全 - 页面链接: https://www.zingnex.cn/en/forum/thread/exist-2026 - Canonical: https://www.zingnex.cn/forum/thread/exist-2026 - Markdown 来源: floors_fallback --- ## [Introduction] EXIST2026: A Multimodal Sexism Detection System Integrating Physiological Signals and Vision-Language Models ### Project Overview - Original Author/Maintainer: ivanarcos02 - Source Platform: GitHub - Release Time: June 2026 - Core Direction: EXIST 2026 Challenge on "People-Centered Multimodal Sexism Detection" ### Core Innovation Combines eye-tracking, heart rate monitoring, EEG signals, and Vision-Language Models (VLM) to build a multimodal system. It uses human physiological and cognitive responses to assist in sexism content detection, breaking through the limitations of traditional text/image analysis. ### Application Scenarios Applicable to content moderation on social media platforms like TikTok, exploring a new "people-centered" paradigm in AI safety. ## Research Background and Problem Definition ### Limitations of Traditional Detection Traditional sexism detection relies on text analysis or image recognition, ignoring the real physiological and cognitive responses when humans perceive discriminatory content. ### Background of EXIST Challenge EXIST (Sexism Identification in Social Networks) is an IberLEF series evaluation task. The 2026 direction focuses on "people-centered multimodal detection", with the core hypothesis: Humans produce measurable physiological responses when viewing discriminatory content, which can be used as detection signals. ### Core Problem How to integrate physiological signals with AI models to achieve more accurate sexism detection that aligns with human feelings? ## Core Innovation: Integration of Multimodal Physiological Signals and VLM ### Physiological Signal Collection 1. **Eye-Tracking**: Analyze fixation distribution, saccade paths, pupil changes, and regression behavior to reflect attention allocation and emotional arousal. 2. **Heart Rate Monitoring**: Capture autonomic nervous system responses via heart rate variability (HRV), heart rate acceleration, and temporal correlation. 3. **EEG**: Extract event-related potentials (ERP), spectral features, and brain region activation to directly measure neural activity. ### Vision-Language Model (VLM) Integrates models like CLIP/BLIP to achieve video frame understanding, cross-modal alignment, context modeling, and extract visual-semantic features. ### Fusion Logic Combine physiological signals with VLM features to build a multimodal detection system, compensating for the shortcomings of single modalities. ## Analysis of Technical Implementation Architecture ### Preprocessing Pipeline - **Time Synchronization**: Align physiological signals with the video timeline - **Signal Filtering**: Remove noise and artifacts - **Feature Extraction**: Extract valid features from raw signals - **Data Cleaning**: Handle missing values and outliers ### Prompt Engineering Design prompt templates to clarify task definition, fine-grained labels (e.g., direct discrimination, micro-discrimination), and context information utilization. ### Experimental Configuration Provide hyperparameter settings, training strategies, and evaluation metrics suitable for sexism detection. ## Scientific Value and Practical Significance ### Methodological Innovation First large-scale application of physiological signals in social media content moderation, pioneering a new "people-centered" AI safety research paradigm that can be extended to other harmful content identification. ### Theoretical Contribution Explore the neurophysiological mechanisms of human perception of sexism, population differences, and consistency between subjective reports and objective indicators. ### Practical Value - Identify gray-area content - Understand the reasons for user discomfort - Optimize content recommendation algorithms ## Technical Challenges and Solutions ### Data Alignment Difficulty Different modalities have large sampling rate differences (video: 30fps / heart rate: 1Hz / EEG: 1000Hz). Solution: Sliding window + interpolation technology to unify the time grid. ### Individual Differences Physiological responses vary by person. Solution: Individual normalization + transfer learning to balance generalization and individual differences. ### Data Sparsity Labeled physiological data is scarce. Solution: Semi-supervised learning + data augmentation to make full use of limited data. ## Ethical Considerations and Data Privacy ### Informed Consent Subjects must fully understand the experiment's purpose (including possible exposure to uncomfortable content) and participate voluntarily. ### Data Privacy Physiological data (especially EEG) is highly identifiable, requiring strict protective measures. ### Research Ethics Balance research value with participants' psychological impact, and set up psychological support mechanisms. ### Application Ethics Alert to technical abuse (e.g., emotional manipulation, improper moderation) and clarify the boundaries of legitimate use. ## Future Directions and Summary ### Future Development 1. **Expand Modalities**: Add galvanic skin response (GSR), facial expression recognition, and speech emotion analysis 2. **Real-Time Detection**: Develop an instant moderation system for live content 3. **Cross-Platform/Cultural**: Verify the generalization of the method on other platforms and in different cultural contexts ### Summary This research breaks through the traditional content moderation paradigm by integrating physiological signals with AI, enabling the system to better understand human feelings. It provides a new direction for AI safety and content platform moderation, and the technology can be extended to mental health, education, and other fields with broad prospects.