H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding

2025-03-31

AI Summary

Existing video understanding benchmarks suffer from three key limitations: restricted temporal coverage (focusing only on short clips), narrow task scope (lacking counterfactual reasoning and trajectory-state tracking), and insufficient viewpoint diversity (overlooking first-person streaming videos). To address these gaps, we propose the first comprehensive benchmark for hierarchical holistic video understanding, supporting multi-scale evaluation across durations from 3 seconds to 1.5 hours, and encompassing both third-person and first-person perspectives. Our method introduces a hierarchical evaluation framework, a multi-granularity annotation protocol, streaming-aware acquisition standards, structured task templates, and cross-temporal reasoning assessment metrics. Systematic evaluation reveals critical bottlenecks in current multimodal large models—particularly in long-horizon temporal modeling, counterfactual comprehension, and first-person trajectory tracking. The benchmark provides a reproducible, multidimensional, and diagnostically rich evaluation platform, along with precise, actionable guidance for advancing video understanding research.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

With the rapid development of multimodal models, the demand for assessing video understanding capabilities has been steadily increasing. However, existing benchmarks for evaluating video understanding exhibit significant limitations in coverage, task diversity, and scene adaptability. These shortcomings hinder the accurate assessment of models' comprehensive video understanding capabilities. To tackle this challenge, we propose a hierarchical and holistic video understanding (H2VU) benchmark designed to evaluate both general video and online streaming video comprehension. This benchmark contributes three key features: Extended video duration: Spanning videos from brief 3-second clips to comprehensive 1.5-hour recordings, thereby bridging the temporal gaps found in current benchmarks. Comprehensive assessment tasks: Beyond traditional perceptual and reasoning tasks, we have introduced modules for countercommonsense comprehension and trajectory state tracking. These additions test the models' deep understanding capabilities beyond mere prior knowledge. Enriched video data: To keep pace with the rapid evolution of current AI agents, we have expanded first-person streaming video datasets. This expansion allows for the exploration of multimodal models' performance in understanding streaming videos from a first-person perspective. Extensive results from H2VU reveal that existing multimodal large language models (MLLMs) possess substantial potential for improvement in our newly proposed evaluation tasks. We expect that H2VU will facilitate advancements in video understanding research by offering a comprehensive and in-depth analysis of MLLMs.

Problem

• Assessing video understanding capabilities with multimodal models
• Addressing limitations in coverage, task diversity, and scene adaptability
• Evaluating general and streaming video comprehension comprehensively

Innovation

• Extended video duration for comprehensive evaluation
• Added countercommonsense and trajectory tracking tasks
• Expanded first-person streaming video datasets