StreamSense: Streaming Social Task Detection with Selective Vision-Language Model Routing

• URL: http://arxiv.org/abs/2601.22738v1
• Date: Fri, 30 Jan 2026 09:19:22 GMT
• Title: StreamSense: Streaming Social Task Detection with Selective Vision-Language Model Routing
• Authors: Han Wang, Deyi Ji, Lanyun Zhu, Jiebo Luo, Roy Ka-Wei Lee,
• Abstract summary: StreamSense is a streaming detector that couples a lightweight streaming encoder with selective routing to a Vision-Language Model expert.<n>We evaluate StreamSense on multiple social streaming detection tasks (e.g., sentiment classification and hate content moderation)<n>Our results indicate that selective escalation and deferral are effective primitives for understanding streaming social tasks.
• Score: 56.32296785595906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Live streaming platforms require real-time monitoring and reaction to social signals, utilizing partial and asynchronous evidence from video, text, and audio. We propose StreamSense, a streaming detector that couples a lightweight streaming encoder with selective routing to a Vision-Language Model (VLM) expert. StreamSense handles most timestamps with the lightweight streaming encoder, escalates hard/ambiguous cases to the VLM, and defers decisions when context is insufficient. The encoder is trained using (i) a cross-modal contrastive term to align visual/audio cues with textual signals, and (ii) an IoU-weighted loss that down-weights poorly overlapping target segments, mitigating label interference across segment boundaries. We evaluate StreamSense on multiple social streaming detection tasks (e.g., sentiment classification and hate content moderation), and the results show that StreamSense achieves higher accuracy than VLM-only streaming while only occasionally invoking the VLM, thereby reducing average latency and compute. Our results indicate that selective escalation and deferral are effective primitives for understanding streaming social tasks. Code is publicly available on GitHub.

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