Can LVLMs Describe Videos like Humans? A Five-in-One Video Annotations Benchmark for Better Human-Machine Comparison

• URL: http://arxiv.org/abs/2410.15270v1
• Date: Sun, 20 Oct 2024 03:59:54 GMT
• Title: Can LVLMs Describe Videos like Humans? A Five-in-One Video Annotations Benchmark for Better Human-Machine Comparison
• Authors: Shiyu Hu, Xuchen Li, Xuzhao Li, Jing Zhang, Yipei Wang, Xin Zhao, Kang Hao Cheong,
• Abstract summary: Video description serves as a fundamental task for evaluating video comprehension, requiring a deep understanding of spatial and temporal dynamics. Current benchmarks for video comprehension have notable limitations, including short video durations, brief annotations, and reliance on a single annotator's perspective. We propose a novel benchmark, FIOVA, designed to evaluate the differences between LVLMs and human understanding more comprehensively.
• Score: 15.363132825156477
• License:
• Abstract: Large vision-language models (LVLMs) have made significant strides in addressing complex video tasks, sparking researchers' interest in their human-like multimodal understanding capabilities. Video description serves as a fundamental task for evaluating video comprehension, necessitating a deep understanding of spatial and temporal dynamics, which presents challenges for both humans and machines. Thus, investigating whether LVLMs can describe videos as comprehensively as humans (through reasonable human-machine comparisons using video captioning as a proxy task) will enhance our understanding and application of these models. However, current benchmarks for video comprehension have notable limitations, including short video durations, brief annotations, and reliance on a single annotator's perspective. These factors hinder a comprehensive assessment of LVLMs' ability to understand complex, lengthy videos and prevent the establishment of a robust human baseline that accurately reflects human video comprehension capabilities. To address these issues, we propose a novel benchmark, FIOVA (Five In One Video Annotations), designed to evaluate the differences between LVLMs and human understanding more comprehensively. FIOVA includes 3,002 long video sequences (averaging 33.6 seconds) that cover diverse scenarios with complex spatiotemporal relationships. Each video is annotated by five distinct annotators, capturing a wide range of perspectives and resulting in captions that are 4-15 times longer than existing benchmarks, thereby establishing a robust baseline that represents human understanding comprehensively for the first time in video description tasks. Using the FIOVA benchmark, we conducted an in-depth evaluation of six state-of-the-art LVLMs, comparing their performance with humans. More detailed information can be found at https://huuuuusy.github.io/fiova/.

Related papers

• StoryTeller: Improving Long Video Description through Global Audio-Visual Character Identification [6.762705315042178]
  Long video description introduces new challenges, such as plot-level consistency across descriptions. We propose StoryTeller, a system for generating dense descriptions of long videos, incorporating both low-level visual concepts and high-level plot information.
  arXiv  Detail & Related papers  (2024-11-11T15:51:48Z)
• Goldfish: Vision-Language Understanding of Arbitrarily Long Videos [51.547065479762715]
  We present a methodology tailored for comprehending videos of arbitrary lengths. We also introduce the TVQA-long benchmark, designed to evaluate models' capabilities in understanding long videos with questions in both vision and text content. Our results indicate that our models have significant improvements in both long and short-video understanding.
  arXiv  Detail & Related papers  (2024-07-17T15:59:32Z)
• 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) We propose VideoNIAH (Video Needle In A Haystack), a benchmark construction framework through synthetic video generation. 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)
• Vript: A Video Is Worth Thousands of Words [54.815686588378156]
  Vript is an annotated corpus of 12K high-resolution videos, offering detailed, dense, and script-like captions for over 420K clips. Each clip has a caption of 145 words, which is over 10x longer than most video-text datasets. Vript is a powerful model capable of end-to-end generation of dense and detailed captions for long videos.
  arXiv  Detail & Related papers  (2024-06-10T06:17:55Z)
• How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs [98.37571997794072]
  We present the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES) CVRR-ES comprehensively assesses the performance of Video-LMMs across 11 diverse real-world video dimensions. Our findings provide valuable insights for building the next generation of human-centric AI systems.
  arXiv  Detail & Related papers  (2024-05-06T17:59:45Z)
• Koala: Key frame-conditioned long video-LLM [70.52369588364992]
  We propose a lightweight and self-supervised long video-LLM (Koala) to adapt pretrained vLLMs for generalizing to longer videos. Our approach outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly, we also empirically show that our approach not only helps a pretrained vLLM to understand long videos but also improves its accuracy on short-term action recognition.
  arXiv  Detail & Related papers  (2024-04-05T18:33:04Z)
• TempCompass: Do Video LLMs Really Understand Videos? [36.28973015469766]
  Existing benchmarks fail to provide a comprehensive feedback on the temporal perception ability of Video LLMs. We propose the textbfTemp benchmark, which introduces a diversity of high-quality temporal aspects and task formats.
  arXiv  Detail & Related papers  (2024-03-01T12:02:19Z)
• VTimeLLM: Empower LLM to Grasp Video Moments [43.51980030572101]
  Large language models (LLMs) have shown remarkable text understanding capabilities. Video LLMs can only provide a coarse description of the entire video. We propose VTimeLLM, a novel Video LLM for fine-grained video moment understanding.
  arXiv  Detail & Related papers  (2023-11-30T10:49:56Z)
• DeVAn: Dense Video Annotation for Video-Language Models [68.70692422636313]
  We present a novel human annotated dataset for evaluating the ability for visual-language models to generate descriptions for real-world video clips. The dataset contains 8.5K YouTube video clips of 20-60 seconds in duration and covers a wide range of topics and interests.
  arXiv  Detail & Related papers  (2023-10-08T08:02:43Z)

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.