GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling

• URL: http://arxiv.org/abs/2404.04763v1
• Date: Sun, 7 Apr 2024 00:28:13 GMT
• Title: GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
• Authors: Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham, Kai-Wei Chang, Nanyun Peng,
• Abstract summary: 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.
• Score: 89.07386210297373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the entire image, the depicted event, and the interactions between various objects participating in the event. Existing models heavily rely on high-quality event-annotated training data to understand the event semantics and structures, and they fail to generalize to new event types and domains. In this paper, we propose GenEARL, a training-free generative framework that harness the power of the modern generative models to understand event task descriptions given image contexts to perform the EARL task. Specifically, GenEARL comprises two stages of generative prompting with a frozen vision-language model (VLM) and a frozen large language model (LLM). First, a generative VLM learns the semantics of the event argument roles and generates event-centric object descriptions based on the image. Subsequently, a LLM is prompted with the generated object descriptions with a predefined template for EARL (i.e., assign an object with an event argument role). 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, respectively. In addition, we outperform CLIP-Event by 22% precision on M2E2 dataset. The framework also allows flexible adaptation and generalization to unseen domains.

Related papers

• EventVL: Understand Event Streams via Multimodal Large Language Model [18.57504605615107]
  We propose EventVL, the first generative event-based MLLM framework for explicit semantic understanding. Specifically, to bridge the data gap for connecting different modalities semantics, we first annotate a large event-image/video-text dataset. To further promote a compact semantic space, Dynamic Semantic Alignment is introduced to improve and complete sparse semantic spaces of events.
  arXiv  Detail & Related papers  (2025-01-23T14:37:21Z)
• EventGPT: Event Stream Understanding with Multimodal Large Language Models [59.65010502000344]
  Event cameras record visual information as asynchronous pixel change streams, excelling at scene perception under unsatisfactory lighting or high-dynamic conditions. Existing multimodal large language models (MLLMs) concentrate on natural RGB images, failing in scenarios where event data fits better. We introduce EventGPT, the first MLLM for event stream understanding.
  arXiv  Detail & Related papers  (2024-12-01T14:38:40Z)
• 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)
• Generating Event-oriented Attribution for Movies via Two-Stage Prefix-Enhanced Multimodal LLM [47.786978666537436]
  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.
  arXiv  Detail & Related papers  (2024-09-14T08:30:59Z)
• VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks [89.24440488456405]
  VisionLLM v2 is an end-to-end generalist multimodal large model (MLLM) It unifies visual perception, understanding, and generation within a single framework.
  arXiv  Detail & Related papers  (2024-06-12T16:44:50Z)
• CLIP-Event: Connecting Text and Images with Event Structures [123.31452120399827]
  We propose a contrastive learning framework to enforce vision-language pretraining models. We take advantage of text information extraction technologies to obtain event structural knowledge. Experiments show that our zero-shot CLIP-Event outperforms the state-of-the-art supervised model in argument extraction.
  arXiv  Detail & Related papers  (2022-01-13T17:03:57Z)

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.