Multimodal Event Detection: Current Approaches and Defining the New Playground through LLMs and VLMs

• URL: http://arxiv.org/abs/2505.10836v1
• Date: Fri, 16 May 2025 04:07:21 GMT
• Title: Multimodal Event Detection: Current Approaches and Defining the New Playground through LLMs and VLMs
• Authors: Abhishek Dey, Aabha Bothera, Samhita Sarikonda, Rishav Aryan, Sanjay Kumar Podishetty, Akshay Havalgi, Gaurav Singh, Saurabh Srivastava,
• Abstract summary: We study the challenges of detecting events on social media, where traditional unimodal systems struggle due to the rapid and multimodal nature of data dissemination.<n>We employ a range of models, including unimodal ModernBERT and ConvNeXt-V2, multimodal fusion techniques, and advanced generative models like GPT-4o, and LLaVA.
• Score: 2.2178611170510694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the challenges of detecting events on social media, where traditional unimodal systems struggle due to the rapid and multimodal nature of data dissemination. We employ a range of models, including unimodal ModernBERT and ConvNeXt-V2, multimodal fusion techniques, and advanced generative models like GPT-4o, and LLaVA. Additionally, we also study the effect of providing multimodal generative models (such as GPT-4o) with a single modality to assess their efficacy. Our results indicate that while multimodal approaches notably outperform unimodal counterparts, generative approaches despite having a large number of parameters, lag behind supervised methods in precision. Furthermore, we also found that they lag behind instruction-tuned models because of their inability to generate event classes correctly. During our error analysis, we discovered that common social media issues such as leet speak, text elongation, etc. are effectively handled by generative approaches but are hard to tackle using supervised approaches.

Related papers

• Continual Multimodal Contrastive Learning [70.60542106731813]
  Multimodal contrastive learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space.<n>However, a critical yet often overlooked challenge remains: multimodal data is rarely collected in a single process, and training from scratch is computationally expensive.<n>In this paper, we formulate CMCL through two specialized principles of stability and plasticity.<n>We theoretically derive a novel optimization-based method, which projects updated gradients from dual sides onto subspaces where any gradient is prevented from interfering with the previously learned knowledge.
  arXiv  Detail & Related papers  (2025-03-19T07:57:08Z)
• See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers [23.701716999879636]
  Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data. We introduce a pioneering framework called the Time Series Anomaly Multimodal Analyzer (TAMA) to enhance both the detection and interpretation of anomalies.
  arXiv  Detail & Related papers  (2024-11-04T10:28:41Z)
• Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench [17.73279547506514]
  We introduce Multimodal Large Language Model Unlearning Benchmark (MLLMU-Bench), a novel benchmark aimed at advancing the understanding of multimodal machine unlearning.<n>MLLMU-Bench consists of 500 fictitious profiles and 153 profiles for public celebrities, each profile feature over 14 customized question-answer pairs, evaluated from both multimodal (image+text) and unimodal (text) perspectives.<n>Surprisingly, our experiments show that unimodal unlearning algorithms excel in generation and cloze tasks, while multimodal unlearning approaches perform better in classification tasks with multimodal inputs.
  arXiv  Detail & Related papers  (2024-10-29T15:07:23Z)
• Multimodal Representation Learning by Alternating Unimodal Adaptation [73.15829571740866]
  We propose MLA (Multimodal Learning with Alternating Unimodal Adaptation) to overcome challenges where some modalities appear more dominant than others during multimodal learning. MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process. It captures cross-modal interactions through a shared head, which undergoes continuous optimization across different modalities. Experiments are conducted on five diverse datasets, encompassing scenarios with complete modalities and scenarios with missing modalities.
  arXiv  Detail & Related papers  (2023-11-17T18:57:40Z)
• MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning [53.90744622542961]
  Reasoning in mathematical domains remains a significant challenge for small language models (LMs) We introduce a new method that exploits existing mathematical problem datasets with diverse annotation styles. Experimental results show that our strategy enables a LLaMA-7B model to outperform prior approaches.
  arXiv  Detail & Related papers  (2023-07-16T05:41:53Z)
• Multi-modal Latent Diffusion [8.316365279740188]
  Multi-modal Variational Autoencoders are a popular family of models that aim to learn a joint representation of the different modalities. Existing approaches suffer from a coherence-quality tradeoff, where models with good generation quality lack generative coherence across modalities. We propose a novel method that uses a set of independently trained, uni-modal, deterministic autoencoders.
  arXiv  Detail & Related papers  (2023-06-07T14:16:44Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias [17.107961913114778]
  multimodal misinformation is a growing problem on social media platforms. In this study, we investigate and identify the presence of unimodal bias in widely-used MMD benchmarks. We introduce a new method -- termed Crossmodal HArd Synthetic MisAlignment (CHASMA) -- for generating realistic synthetic training data.
  arXiv  Detail & Related papers  (2023-04-27T12:28:29Z)
• $\ extit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation [65.29170569821093]
  parallel text generation has received widespread attention due to its success in generation efficiency. In this paper, we propose $textitlatent$-GLAT, which employs the discrete latent variables to capture word categorical information. Experiment results show that our method outperforms strong baselines without the help of an autoregressive model.
  arXiv  Detail & Related papers  (2022-04-05T07:34:12Z)
• Multimodal Categorization of Crisis Events in Social Media [81.07061295887172]
  We present a new multimodal fusion method that leverages both images and texts as input. In particular, we introduce a cross-attention module that can filter uninformative and misleading components from weak modalities. We show that our method outperforms the unimodal approaches and strong multimodal baselines by a large margin on three crisis-related tasks.
  arXiv  Detail & Related papers  (2020-04-10T06:31:30Z)

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.