Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding

• URL: http://arxiv.org/abs/2506.06275v1
• Date: Fri, 06 Jun 2025 17:58:36 GMT
• Title: Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding
• Authors: Emmanouil Zaranis, António Farinhas, Saul Santos, Beatriz Canaverde, Miguel Moura Ramos, Aditya K Surikuchi, André Viveiros, Baohao Liao, Elena Bueno-Benito, Nithin Sivakumaran, Pavlo Vasylenko, Shoubin Yu, Sonal Sannigrahi, Wafaa Mohammed, Ben Peters, Danae Sánchez Villegas, Elias Stengel-Eskin, Giuseppe Attanasio, Jaehong Yoon, Stella Frank, Alessandro Suglia, Chrysoula Zerva, Desmond Elliott, Mariella Dimiccoli, Mohit Bansal, Oswald Lanz, Raffaella Bernardi, Raquel Fernández, Sandro Pezzelle, Vlad Niculae, André F. T. Martins,
• Abstract summary: We introduce MF$2$, a new benchmark for evaluating whether models can comprehend, consolidate, and recall key narrative information from full-length movies.<n>For each pair, models must correctly identify both the true and false claims.<n>Our experiments demonstrate that both open-weight and closed state-of-the-art models fall well short of human performance.
• Score: 97.05584099530226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite recent progress in vision-language models (VLMs), holistic understanding of long-form video content remains a significant challenge, partly due to limitations in current benchmarks. Many focus on peripheral, ``needle-in-a-haystack'' details, encouraging context-insensitive retrieval over deep comprehension. Others rely on large-scale, semi-automatically generated questions (often produced by language models themselves) that are easier for models to answer but fail to reflect genuine understanding. In this paper, we introduce MF$^2$, a new benchmark for evaluating whether models can comprehend, consolidate, and recall key narrative information from full-length movies (50-170 minutes long). MF$^2$ includes over 50 full-length, open-licensed movies, each paired with manually constructed sets of claim pairs -- one true (fact) and one plausible but false (fib), totalling over 850 pairs. These claims target core narrative elements such as character motivations and emotions, causal chains, and event order, and refer to memorable moments that humans can recall without rewatching the movie. Instead of multiple-choice formats, we adopt a binary claim evaluation protocol: for each pair, models must correctly identify both the true and false claims. This reduces biases like answer ordering and enables a more precise assessment of reasoning. Our experiments demonstrate that both open-weight and closed state-of-the-art models fall well short of human performance, underscoring the relative ease of the task for humans and their superior ability to retain and reason over critical narrative information -- an ability current VLMs lack.

Related papers

• VersaVid-R1: A Versatile Video Understanding and Reasoning Model from Question Answering to Captioning Tasks [44.30048178589923]
  We introduce two novel datasets designed to stimulate the model's advanced video understanding and reasoning abilities.<n>We develop VersaVid-R1, the first versatile video understanding and reasoning model under the Reason-Then-Respond paradigm.
  arXiv  Detail & Related papers  (2025-06-10T03:57:53Z)
• Video-Holmes: Can MLLM Think Like Holmes for Complex Video Reasoning? [56.06537213958482]
  We present Video-Holmes, a benchmark designed to evaluate the complex video reasoning capabilities of MLLMs.<n>Video-Holmes consists of 1,837 questions derived from 270 manually annotated suspense short films.<n>Our comprehensive evaluation of state-of-the-art MLLMs reveals that, while these models generally excel at visual perception, they encounter substantial difficulties with integrating information.
  arXiv  Detail & Related papers  (2025-05-27T16:05:01Z)
• MINERVA: Evaluating Complex Video Reasoning [72.12644008002566]
  We provide a new video reasoning dataset called MINERVA for modern multimodal models.<n>Our dataset is multimodal, diverse in terms of video domain and length, and consists of complex multi-step questions.<n>We perform fine-grained error analysis to identify common failure modes across various models, and create a taxonomy of reasoning errors.
  arXiv  Detail & Related papers  (2025-05-01T17:41:49Z)
• 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.<n>They struggle with fine-grained understanding, particularly in aspects such as visual dynamics and video details inquiries.<n>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)
• FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning [15.363132825156477]
  We introduce FIOVA, a human-centric benchmark tailored for evaluation of large vision-language models (LVLMs)<n>It comprises 3,002 real-world videos (about 33.6s each), each annotated independently by five annotators.<n>We propose FIOVA-DQ, an event-level evaluation metric that incorporates cognitive weights derived from annotator consensus.
  arXiv  Detail & Related papers  (2024-10-20T03:59:54Z)
• 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)
• One Thousand and One Pairs: A "novel" challenge for long-context language models [56.60667988954638]
  NoCha is a dataset of 1,001 pairs of true and false claims about 67 fictional books. Our annotators confirm that the largest share of pairs in NoCha require global reasoning over the entire book to verify. On average, models perform much better on pairs that require only sentence-level retrieval vs. global reasoning.
  arXiv  Detail & Related papers  (2024-06-24T02:03:57Z)
• Investigating Video Reasoning Capability of Large Language Models with Tropes in Movies [69.28082193942991]
  This paper introduces a novel dataset, Tropes in Movies (TiM), designed as a testbed for exploring two critical yet previously overlooked video reasoning skills. utilizing tropes from movie storytelling, TiM evaluates the reasoning capabilities of state-of-the-art LLM-based approaches. To address these deficiencies, we propose Face-Enhanced Viper of Role Interactions (FEVoRI) and Context Query Reduction (ConQueR)
  arXiv  Detail & Related papers  (2024-06-16T12:58:31Z)
• Fill-in-the-blank as a Challenging Video Understanding Evaluation Framework [19.031957183047048]
  We introduce a novel dataset consisting of 28,000 videos and fill-in-the-blank tests. We show that both a multimodal model and a strong language model have a large gap with human performance.
  arXiv  Detail & Related papers  (2021-04-09T04:00:10Z)

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.