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

• URL: http://arxiv.org/abs/2503.24008v2
• Date: Tue, 27 May 2025 02:04:52 GMT
• Title: H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding
• Authors: Qi Wu, Quanlong Zheng, Yanhao Zhang, Junlin Xie, Jinguo Luo, Kuo Wang, Peng Liu, Qingsong Xie, Ru Zhen, Zhenyu Yang, Haonan Lu,
• Abstract summary: Existing benchmarks for evaluating video understanding exhibit significant limitations in coverage, task diversity, and scene adaptability.<n>We propose a hierarchical and holistic video understanding benchmark designed to evaluate both general video and online streaming video comprehension.<n>This benchmark contributes three key features: extended video duration, comprehensive assessment tasks, andEnriched video data.
• Score: 25.111988967973147
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• HV-MMBench: Benchmarking MLLMs for Human-Centric Video Understanding [79.06209664703258]
  Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks involving both images and videos.<n>Existing human-centric benchmarks predominantly emphasize video generation quality and action recognition, while overlooking essential perceptual and cognitive abilities required in human-centered scenarios.<n>We propose a rigorously curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric video understanding.
  arXiv  Detail & Related papers  (2025-07-07T11:52:24Z)
• Video-CoT: A Comprehensive Dataset for Spatiotemporal Understanding of Videos Based on Chain-of-Thought [19.792159494513424]
  Video comprehension is essential for various applications ranging from video analysis to interactive systems.<n>Despite advancements in vision-language models, these models often struggle to capture nuanced,temporal details.<n>To address this, we introduce Video-Thought, a groundbreaking dataset designed to enhance video understanding.
  arXiv  Detail & Related papers  (2025-06-10T14:08:56Z)
• SAMA: Towards Multi-Turn Referential Grounded Video Chat with Large Language Models [80.3895950009792]
  Achieving fine-grained-temporal understanding in videos remains a major challenge for current Video Large Multimodels (Video LMMs)<n>We contribute in three core aspects: dataset, model, and benchmark.<n>First, we introduce SAMA-239K, a large-scale dataset comprising 15K videos specifically to enable joint learning of video understanding, grounding, and multi-turn video chat.<n>Second, we propose the SAMA model, which incorporates a versatile-temporal context aggregator and a Segment Model to jointly enhance fine-grained video comprehension and precise grounding capabilities.
  arXiv  Detail & Related papers  (2025-05-24T18:13:16Z)
• SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding [23.96372422130216]
  Video-based Large Language Models (VideoVid-LLMs) have witnessed substantial advancements in recent years. They struggle with fine-grained understanding, particularly in aspects such as visual dynamics and video details inquiries. To tackle these shortcomings, we find that fine-tuning Video-LLMs on self-supervised fragment tasks greatly improve their fine-grained video understanding abilities.
  arXiv  Detail & Related papers  (2025-04-10T13:40:34Z)
• MomentSeeker: A Comprehensive Benchmark and A Strong Baseline For Moment Retrieval Within Long Videos [62.01402470874109]
  We present MomentSeeker, a benchmark to evaluate retrieval models' performance in handling general long-video moment retrieval tasks. It incorporates long videos of over 500 seconds on average, making it the first benchmark specialized for long-video moment retrieval. It covers a wide range of task categories (including Moment Search, Caption Alignment, Image-conditioned Moment Search, and Video-conditioned Moment Search) and diverse application scenarios. We further fine-tune an MLLM-based LVMR retriever on synthetic data, which demonstrates strong performance on our benchmark.
  arXiv  Detail & Related papers  (2025-02-18T05:50:23Z)
• VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM [81.15525024145697]
  Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding.<n>However, they mainly focus on holistic comprehension and struggle with capturing fine-grained spatial and temporal details.<n>We introduce the VideoRefer Suite to empower Video LLM for finer-level spatial-temporal video understanding.
  arXiv  Detail & Related papers  (2024-12-31T18:56:46Z)
• SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis [52.050036778325094]
  We introduce SALOVA: Segment-Augmented Video Assistant, a novel video-LLM framework designed to enhance the comprehension of lengthy video content. We present a high-quality collection of 87.8K long videos, each densely captioned at the segment level to enable models to capture scene continuity and maintain rich context. Our framework mitigates the limitations of current video-LMMs by allowing for precise identification and retrieval of relevant video segments in response to queries.
  arXiv  Detail & Related papers  (2024-11-25T08:04:47Z)
• TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models [75.42002690128486]
  TemporalBench is a new benchmark dedicated to evaluating fine-grained temporal understanding in videos. It consists of 10K video question-answer pairs, derived from 2K high-quality human annotations detailing the temporal dynamics in video clips. Results show that state-of-the-art models like GPT-4o achieve only 38.5% question answering accuracy on TemporalBench.
  arXiv  Detail & Related papers  (2024-10-14T17:59:58Z)
• MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding [67.56182262082729]
  We introduce MMBench-Video, a quantitative benchmark to rigorously evaluate large vision-language models (LVLMs) in video understanding. MMBench-Video incorporates lengthy videos from YouTube and employs free-form questions, mirroring practical use cases. The benchmark is meticulously crafted to probe the models' temporal reasoning skills, with all questions human-annotated according to a carefully constructed ability taxonomy.
  arXiv  Detail & Related papers  (2024-06-20T17:26:01Z)
• Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs [20.168429351519055]
  Video understanding is a crucial next step for multimodal large language models (LMLMs)<n>We propose VideoNIAH (Video Needle In A Haystack), a benchmark construction framework through synthetic video generation.<n>We conduct a comprehensive evaluation of both proprietary and open-source models, uncovering significant differences in their video understanding capabilities.
  arXiv  Detail & Related papers  (2024-06-13T17:50:05Z)
• MVBench: A Comprehensive Multi-modal Video Understanding Benchmark [63.14000659130736]
  We introduce a comprehensive Multi-modal Video understanding Benchmark, namely MVBench. We first introduce a novel static-to-dynamic method to define these temporal-related tasks. Then, guided by the task definition, we automatically convert public video annotations into multiple-choice QA to evaluate each task.
  arXiv  Detail & Related papers  (2023-11-28T17:59:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.