Datasets:
Tasks:
Video Classification
Modalities:
Video
Languages:
English
Size:
< 1K
Tags:
temporal-localization
video-understanding
multimodal
multimodal-safety
content-moderation
long-video
License:
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Verified signatureTHVL-Bench: A Multimodal Benchmark for Temporal Localization of Harmful Content in Long Videos. Description: THVL-Bench is a large-scale multimodal benchmark designed for the task of Temporal Harmful Video Localization (THVL). While traditional video safety tools focus on video-level classification, THVL-Bench requires models to identify the exact start and end times of harmful segments within long-form videos and provide multimodal rationales for their decisions . The dataset contains 450 real-world long videos collected from platforms like YouTube and Bilibili, featuring 1,099 manually annotated segments across 11 distinct harm categories . Dataset Structure: The dataset is provided in a structured format (CSV/Parquet) with the following key columns: videoID: The unique identifier for the video (e.g., Bilibili BV IDs or YouTube IDs). segment-level annotation: A list of binary vectors representing the presence of 11 harm categories for each segment. segment-level timestamp: The precis…7bbb0f6 -
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Verified signatureTHVL-Bench: A Multimodal Benchmark for Temporal Localization of Harmful Content in Long Videos. Description: THVL-Bench is a large-scale multimodal benchmark designed for the task of Temporal Harmful Video Localization (THVL). While traditional video safety tools focus on video-level classification, THVL-Bench requires models to identify the exact start and end times of harmful segments within long-form videos and provide multimodal rationales for their decisions . The dataset contains 450 real-world long videos collected from platforms like YouTube and Bilibili, featuring 1,099 manually annotated segments across 11 distinct harm categories . Dataset Structure: The dataset is provided in a structured format (CSV/Parquet) with the following key columns: videoID: The unique identifier for the video (e.g., Bilibili BV IDs or YouTube IDs). segment-level annotation: A list of binary vectors representing the presence of 11 harm categories for each segment. segment-level timestamp: The precis…bed9a0f -
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Verified signatureTHVL-Bench: A Multimodal Benchmark for Temporal Localization of Harmful Content in Long Videos. Description: THVL-Bench is a large-scale multimodal benchmark designed for the task of Temporal Harmful Video Localization (THVL). While traditional video safety tools focus on video-level classification, THVL-Bench requires models to identify the exact start and end times of harmful segments within long-form videos and provide multimodal rationales for their decisions . The dataset contains 450 real-world long videos collected from platforms like YouTube and Bilibili, featuring 1,099 manually annotated segments across 11 distinct harm categories . Dataset Structure: The dataset is provided in a structured format (CSV/Parquet) with the following key columns: videoID: The unique identifier for the video (e.g., Bilibili BV IDs or YouTube IDs). segment-level annotation: A list of binary vectors representing the presence of 11 harm categories for each segment. segment-level timestamp: The pre…0e5e91a
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Verified signatureTHVL-Bench: A Multimodal Benchmark for Temporal Localization of Harmful Content in Long Videos ## Introduction Video-level safety assessment is often insufficient for real-world moderation because harmful content is typically sparse and temporally localized. We introduce **Temporal Harmful Video Localization (THVL)**, a task requiring models to identify harmful segments with precise temporal boundaries and multimodal rationales. ## Dataset Summary **THVL-Bench** is a comprehensive benchmark designed to evaluate the capability of Multimodal Large Language Models (MLLMs) in detecting and localizing harmful content within long-form videos. - **Total Videos**: 450 real-world long videos sourced from YouTube and Bilibili. - **Annotations**: 1,099 manually annotated segments with precise start/end timestamps. - **Categories**: 11 distinct harm categories reflecting diverse online safety challenges. - **Key Feature**: Each segment includes multimodal rationales (visual/audio) explaining the…1feee27
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Wenjie JiangVerified signaturebdf7372