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cs.CV2026

Video2Reaction: Training Foundation Video Models to Predict Audience Reaction

Sidong Zhang, Trang Nguyen, Shiv Shankar +4

We introduce Video2Reaction, a multimodal dataset that maps short movie segments to the induced emotional reactions of viewers in the wild, as expressed through social media commen…

cs.CV2026

Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

Trang Nguyen, Sidong Zhang, Shiv Shankar +4

Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis. To enable audience reactio…

cs.CV2026

OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization

Sakib Reza, Gauri Jagatap, Mohsen Moghaddam +2

Temporal Action Localization (TAL) typically relies on segment annotations or offline access to full videos, limiting scalability and online use. We introduce Point-Supervised Onli…

cs.CV2025

Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings

Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti +6

Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual information during multimodal reasoning.…

cs.CV2025

Moment Sampling in Video LLMs for Long-Form Video QA

Mustafa Chasmai, Gauri Jagatap, Gouthaman KV +3

Recent advancements in video large language models (Video LLMs) have significantly advanced the field of video question answering (VideoQA). While existing methods perform well on…