ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos

• URL: http://arxiv.org/abs/2406.19392v2
• Date: Tue, 2 Jul 2024 15:30:03 GMT
• Title: ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos
• Authors: Jr-Jen Chen, Yu-Chien Liao, Hsi-Che Lin, Yu-Chu Yu, Yen-Chun Chen, Yu-Chiang Frank Wang,
• Abstract summary: ReXTime is a benchmark designed to rigorously test AI models' ability to perform temporal reasoning within video events. We develop an automated pipeline for generating temporal reasoning question-answer pairs. Our pipeline creates a training dataset of 9,695 machine generated samples without manual effort.
• Score: 24.502570960589182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce ReXTime, a benchmark designed to rigorously test AI models' ability to perform temporal reasoning within video events. Specifically, ReXTime focuses on reasoning across time, i.e. human-like understanding when the question and its corresponding answer occur in different video segments. This form of reasoning, requiring advanced understanding of cause-and-effect relationships across video segments, poses significant challenges to even the frontier multimodal large language models. To facilitate this evaluation, we develop an automated pipeline for generating temporal reasoning question-answer pairs, significantly reducing the need for labor-intensive manual annotations. Our benchmark includes 921 carefully vetted validation samples and 2,143 test samples, each manually curated for accuracy and relevance. Evaluation results show that while frontier large language models outperform academic models, they still lag behind human performance by a significant 14.3% accuracy gap. Additionally, our pipeline creates a training dataset of 9,695 machine generated samples without manual effort, which empirical studies suggest can enhance the across-time reasoning via fine-tuning.

Related papers

• Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision [120.40788744292739]
  We propose a two-player paradigm that separates the roles of reasoning and critique models. We first propose AutoMathCritique, an automated and scalable framework for collecting critique data. We demonstrate that the critique models consistently improve the actor's performance on difficult queries at test-time.
  arXiv  Detail & Related papers  (2024-11-25T17:11:54Z)
• 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)
• Temporal Reasoning Transfer from Text to Video [51.68487044397409]
  Video Large Language Models (Video LLMs) struggle with tracking temporal changes and reasoning about temporal relationships. We introduce the Textual Temporal reasoning Transfer (T3) to transfer temporal reasoning abilities from text to video domains. LongVA-7B model achieves competitive performance on comprehensive video benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T16:10:29Z)
• E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding [57.630136434038384]
  We introduce E.T. Bench (Event-Level & Time-Sensitive Video Understanding Benchmark), a large-scale benchmark for open-ended event-level video understanding. We extensively evaluated 8 Image-LLMs and 12 Video-LLMs on our benchmark, and the results reveal that state-of-the-art models for coarse-level (video-level) understanding struggle to solve our fine-grained tasks. Our simple but effective solution demonstrates superior performance in multiple scenarios.
  arXiv  Detail & Related papers  (2024-09-26T17:53:04Z)
• Combating Missing Modalities in Egocentric Videos at Test Time [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues. We propose a novel approach to address this issue at test time without requiring retraining. MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Test of Time: Instilling Video-Language Models with a Sense of Time [42.290970800790184]
  Seven existing video-language models struggle to understand simple temporal relations. We propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We observe encouraging performance gains especially when the task needs higher time awareness.
  arXiv  Detail & Related papers  (2023-01-05T14:14:36Z)
• Ripple: Concept-Based Interpretation for Raw Time Series Models in Education [5.374524134699487]
  Time series is the most prevalent form of input data for educational prediction tasks. We propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy. We analyze these advances in the education domain, addressing the task of early student performance prediction.
  arXiv  Detail & Related papers  (2022-12-02T12:26:00Z)
• CONVIQT: Contrastive Video Quality Estimator [63.749184706461826]
  Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.
  arXiv  Detail & Related papers  (2022-06-29T15:22:01Z)

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.