Generating Event-oriented Attribution for Movies via Two-Stage Prefix-Enhanced Multimodal LLM

• URL: http://arxiv.org/abs/2409.09362v1
• Date: Sat, 14 Sep 2024 08:30:59 GMT
• Title: Generating Event-oriented Attribution for Movies via Two-Stage Prefix-Enhanced Multimodal LLM
• Authors: Yuanjie Lyu, Tong Xu, Zihan Niu, Bo Peng, Jing Ke, Enhong Chen,
• Abstract summary: We propose a Two-Stage Prefix-Enhanced MLLM (TSPE) approach for event attribution in movie videos. In the local stage, we introduce an interaction-aware prefix that guides the model to focus on the relevant multimodal information within a single clip. In the global stage, we strengthen the connections between associated events using an inferential knowledge graph.
• Score: 47.786978666537436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prosperity of social media platforms has raised the urgent demand for semantic-rich services, e.g., event and storyline attribution. However, most existing research focuses on clip-level event understanding, primarily through basic captioning tasks, without analyzing the causes of events across an entire movie. This is a significant challenge, as even advanced multimodal large language models (MLLMs) struggle with extensive multimodal information due to limited context length. To address this issue, we propose a Two-Stage Prefix-Enhanced MLLM (TSPE) approach for event attribution, i.e., connecting associated events with their causal semantics, in movie videos. In the local stage, we introduce an interaction-aware prefix that guides the model to focus on the relevant multimodal information within a single clip, briefly summarizing the single event. Correspondingly, in the global stage, we strengthen the connections between associated events using an inferential knowledge graph, and design an event-aware prefix that directs the model to focus on associated events rather than all preceding clips, resulting in accurate event attribution. Comprehensive evaluations of two real-world datasets demonstrate that our framework outperforms state-of-the-art methods.

Related papers

• Grounding Partially-Defined Events in Multimodal Data [61.0063273919745]
  We introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. We propose a benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.
  arXiv  Detail & Related papers  (2024-10-07T17:59:48Z)
• Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models [41.524192769406945]
  Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events. Existing approaches utilize fine-tuning of small language models (SLMs) to address the compatibility among the contexts of event mentions. We propose a collaborative approach for CDECR, leveraging the capabilities of both a universally capable LLM and a task-specific SLM.
  arXiv  Detail & Related papers  (2024-06-04T09:35:47Z)
• GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling [89.07386210297373]
  GenEARL is a training-free generative framework that harnesses the power of modern generative models to understand event task descriptions. We show that GenEARL outperforms the contrastive pretraining (CLIP) baseline by 9.4% and 14.2% accuracy for zero-shot EARL on the M2E2 and SwiG datasets.
  arXiv  Detail & Related papers  (2024-04-07T00:28:13Z)
• Leveraging the Video-level Semantic Consistency of Event for Audio-visual Event Localization [8.530561069113716]
  We propose a novel video-level semantic consistency guidance network for the AVE localization task. It consists of two components: a cross-modal event representation extractor and an intra-modal semantic consistency enhancer. We perform extensive experiments on the public AVE dataset and outperform the state-of-the-art methods in both fully- and weakly-supervised settings.
  arXiv  Detail & Related papers  (2022-10-11T08:15:57Z)
• Unifying Event Detection and Captioning as Sequence Generation via Pre-Training [53.613265415703815]
  We propose a unified pre-training and fine-tuning framework to enhance the inter-task association between event detection and captioning. Our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data.
  arXiv  Detail & Related papers  (2022-07-18T14:18:13Z)
• Beyond Grounding: Extracting Fine-Grained Event Hierarchies Across Modalities [43.048896440009784]
  We propose the task of extracting event hierarchies from multimodal (video and text) data. This reveals the structure of events and is critical to understanding them. We show the limitations of state-of-the-art unimodal and multimodal baselines on this task.
  arXiv  Detail & Related papers  (2022-06-14T23:24:15Z)
• Multi-Modulation Network for Audio-Visual Event Localization [138.14529518908736]
  We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the segment level. We propose a novel MultiModulation Network (M2N) to learn the above correlation and leverage it as semantic guidance.
  arXiv  Detail & Related papers  (2021-08-26T13:11:48Z)
• Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning [63.91369308085091]
  We propose a novel and simple model for event sequence generation and explore temporal relationships of the event sequence in the video. The proposed model omits inefficient two-stage proposal generation and directly generates event boundaries conditioned on bi-directional temporal dependency in one pass. The overall system achieves state-of-the-art performance on the dense-captioning events in video task with 9.894 METEOR score on the challenge testing set.
  arXiv  Detail & Related papers  (2020-06-14T13:21:37Z)

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.