Understanding Long Videos in One Multimodal Language Model Pass

• URL: http://arxiv.org/abs/2403.16998v1
• Date: Mon, 25 Mar 2024 17:59:09 GMT
• Title: Understanding Long Videos in One Multimodal Language Model Pass
• Authors: Kanchana Ranasinghe, Xiang Li, Kumara Kahatapitiya, Michael S. Ryoo,
• Abstract summary: Large Language Models (LLMs) are known to contain a strong awareness of world knowledge. We propose Likelihood Selection, a technique that unlocks faster inference in autoregressive LLMs. Our resulting Multimodal Video Understanding framework demonstrates state-of-the-art performance across long-video and fine-grained action recognition benchmarks.
• Score: 44.78900245769057
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Large Language Models (LLMs), known to contain a strong awareness of world knowledge, have allowed recent approaches to achieve excellent performance on Long-Video Understanding benchmarks, but at high inference costs. In this work, we first propose Likelihood Selection, a simple technique that unlocks faster inference in autoregressive LLMs for multiple-choice tasks common in long-video benchmarks. In addition to faster inference, we discover the resulting models to yield surprisingly good accuracy on long-video tasks, even with no video specific information. Building on this, we inject video-specific object-centric information extracted from off-the-shelf pre-trained models and utilize natural language as a medium for information fusion. Our resulting Multimodal Video Understanding (MVU) framework demonstrates state-of-the-art performance across long-video and fine-grained action recognition benchmarks. Code available at: https://github.com/kahnchana/mvu

Related papers

• 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)
• LVBench: An Extreme Long Video Understanding Benchmark [38.839913137854104]
  We introduce LVBench, a benchmark specifically designed for long video understanding. Our dataset comprises publicly sourced videos and encompasses a diverse set of tasks aimed at long video comprehension and information extraction.
  arXiv  Detail & Related papers  (2024-06-12T09:36:52Z)
• MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding [66.56100008577134]
  This study focuses on designing an efficient and effective model for long-term video understanding. We propose to process videos in an online manner and store past video information in a memory bank. Our model can achieve state-of-the-art performances across multiple datasets.
  arXiv  Detail & Related papers  (2024-04-08T17:59:24Z)
• LongVLM: Efficient Long Video Understanding via Large Language Models [55.813206751150716]
  LongVLM is a simple yet powerful VideoLLM for long video understanding. We encode video representations that incorporate both local and global information. Our model produces more precise responses for long video understanding.
  arXiv  Detail & Related papers  (2024-04-04T11:33:29Z)
• 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)
• Revealing Single Frame Bias for Video-and-Language Learning [115.01000652123882]
  We show that a single-frame trained model can achieve better performance than existing methods that use multiple frames for training. This result reveals the existence of a strong "static appearance bias" in popular video-and-language datasets. We propose two new retrieval tasks based on existing fine-grained action recognition datasets that encourage temporal modeling.
  arXiv  Detail & Related papers  (2022-06-07T16:28:30Z)
• VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation [124.02278735049235]
  VALUE benchmark aims to cover a broad range of video genres, video lengths, data volumes, and task difficulty levels. We evaluate various baseline methods with and without large-scale VidL pre-training. The significant gap between our best model and human performance calls for future study for advanced VidL models.
  arXiv  Detail & Related papers  (2021-06-08T18:34:21Z)

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.