Cultural understanding in video means more than recognizing what is visible; it requires grasping the symbolic and temporal significance of cultural concepts. We decompose this into three abilities: naming what a concept symbolizes, visually recognizing it on video, and locating its sub-events in time. Existing video-cultural benchmarks tend to test what is seen, collapsing these three abilities into a single score that hides the bottleneck. We introduce the Cultural Moment Benchmark (CMB): 306 expert-curated concepts from seven countries in Southeast Asia across five categories. We evaluate each concept through three stages, one per ability. Given a description, Stage 1 (S1) selects from four candidate concept names, Stage 2 (S2) selects from four candidate video moments, and Stage 3 (S3) predicts the start and end times of the moment in a video. To keep each stage focused on a distinct ability, we use three design choices: semantic-similarity distractors (S1, S2), unlabeled video moments (S2), and free-form localization on a different example video (S3). Across six vision-language models, failure modes vary by ability and modality. i) Even the strongest closed-source models score below 30% when all three stages must be correct; ii) The three abilities do not fully cascade: naming a concept correctly helps half the models recognize it on video, but recognizing it has little effect on locating the sub-event in time; iii) Audio is complementary, redundant, or distracting depending on the concept, more often distracting in non-Latin-script countries; removing both audio and subtitles hurts Games and Music the most. Our 14-rater human study shows that even Expert raters score below chance on concepts from a neighboring country, indicating that CMB requires country-specific cultural knowledge. CMB acts as a diagnostic harness, attributing failures to a specific ability or modality.
NARU, a benchmark designed to evaluate Narrative evolution and Reasoning on cultural Understanding in Japanese long-form video, is introduced, a hierarchical memory-based annotation pipeline that transforms raw video into structured event, narrative, and cultural annotations, then generates questions via task-oriented...
Yu-Heng Huang, Jian-Lang Chen, Jia-Yang Song et al.· 0 citations
Results show that although current models achieve strong semantic adherence and visual quality, they often fail to faithfully capture fine-grained cultural details, particularly for underrepresented regions, rituals, and multimodal cultural cues.
Xian-Jing Han, Yuhan Su, Yang Deng et al.· 2 citations
This study introduces MVSCBench, the benchmark designed to evaluate multimodal AI models’ ability to interpret symbolic and cultural meanings in music videos. Using K-pop music video clips, we organize music video understanding into three stages: surface perception, symbolic interpretation, and cultural grounding. Base...
VideoVIBE is introduced, a video-grounded benchmark that transforms human-operated webpage recordings into fine-grained diagnostic tasks and V2Lens is proposed, a training-free, evidence-grounded multi-agent system that challenges and selectively refines initial video-based diagnoses through targeted visual and source-...
Jia-Jun Xu, Yang-Hao Zhou, Jing Liao et al.· 1 citation
DVBench is introduced, a benchmark for evaluating MLLMs on data videos, a storytelling medium that integrates dynamic charts with structured narratives, and decompose data video understanding into five dimensions, identifying two notable phenomena.
Bomiao Wang, Zekai Shao, Jiexiang Lan et al.· 0 citations
The proposed Temporal Tree of Thought T^3, a training-free framework for adaptive coarse-to-fine long-video understanding, constructs a question-agnostic hierarchical temporal tree via recursive temporally constrained clustering, where each node represents a contiguous segment with an informative key frame.
Ziling Huang, Shin'ichi Satoh· 0 citations
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