Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis

• URL: http://arxiv.org/abs/2504.10351v1
• Date: Mon, 14 Apr 2025 16:00:57 GMT
• Title: Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis
• Authors: Kaiwen Zheng, Xuri Ge, Junchen Fu, Jun Peng, Joemon M. Jose,
• Abstract summary: We present a comprehensive pipeline for multimodal facial state analysis.<n>We introduce a novel Multilevel Multimodal Face Foundation model (MF2) tailored for Action Unit (AU) and emotion recognition.<n>Experimentation show superior performance for AU and emotion detection tasks.
• Score: 5.795431510723275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal foundation models have significantly improved feature representation by integrating information from multiple modalities, making them highly suitable for a broader set of applications. However, the exploration of multimodal facial representation for understanding perception has been limited. Understanding and analyzing facial states, such as Action Units (AUs) and emotions, require a comprehensive and robust framework that bridges visual and linguistic modalities. In this paper, we present a comprehensive pipeline for multimodal facial state analysis. First, we compile a new Multimodal Face Dataset (MFA) by generating detailed multilevel language descriptions of face, incorporating Action Unit (AU) and emotion descriptions, by leveraging GPT-4o. Second, we introduce a novel Multilevel Multimodal Face Foundation model (MF^2) tailored for Action Unit (AU) and emotion recognition. Our model incorporates comprehensive visual feature modeling at both local and global levels of face image, enhancing its ability to represent detailed facial appearances. This design aligns visual representations with structured AU and emotion descriptions, ensuring effective cross-modal integration. Third, we develop a Decoupled Fine-Tuning Network (DFN) that efficiently adapts MF^2 across various tasks and datasets. This approach not only reduces computational overhead but also broadens the applicability of the foundation model to diverse scenarios. Experimentation show superior performance for AU and emotion detection tasks.

Related papers

• FaceLLM: A Multimodal Large Language Model for Face Understanding [22.8742248559748]
  We introduce FaceLLM, a multimodal large language model trained specifically for facial image understanding.<n>To construct the training data, we propose a novel weakly supervised pipeline that uses ChatGPT with attribute-aware prompts to generate high-quality question-answer pairs.<n>Our experiments demonstrate that FaceLLM improves the performance of MLLMs on various face-centric tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-07-14T14:04:14Z)
• Facial Emotion Learning with Text-Guided Multiview Fusion via Vision-Language Model for 3D/4D Facial Expression Recognition [1.03341388090561]
  Facial expression recognition (FER) in 3D and 4D domains presents a significant challenge in affective computing.<n>We propose FACET-VLM, a vision-language framework for 3D/4D FER that integrates multiview facial representation learning with semantic guidance from natural language prompts.<n>Our model achieves state-of-the-art accuracy across multiple benchmarks, including BU-3DFE, Bosphorus, BU-4DFE, and BP4D-Spontaneous.
  arXiv  Detail & Related papers  (2025-07-02T12:55:09Z)
• Multi-SpatialMLLM: Multi-Frame Spatial Understanding with Multi-Modal Large Language Models [70.41727912081463]
  Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single images.<n>We propose a framework to equip MLLMs with robust multi-frame spatial understanding by integrating depth perception, visual correspondence, and dynamic perception.<n>Our model, Multi-SpatialMLLM, achieves significant gains over baselines and proprietary systems, demonstrating scalable, generalizable multi-frame reasoning.
  arXiv  Detail & Related papers  (2025-05-22T17:59:39Z)
• FaceInsight: A Multimodal Large Language Model for Face Perception [69.06084304620026]
  We propose FaceInsight, a versatile face perception large language model (MLLM) that provides fine-grained facial information. Our approach introduces visual-textual alignment of facial knowledge to model both uncertain dependencies and deterministic relationships among facial information. Comprehensive experiments and analyses across three face perception tasks demonstrate that FaceInsight consistently outperforms nine compared MLLMs.
  arXiv  Detail & Related papers  (2025-04-22T06:31:57Z)
• Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment [58.94611347128066]
  Task Preference Optimization (TPO) is a novel method that utilizes differentiable task preferences derived from typical fine-grained visual tasks. By leveraging rich visual labels during training, TPO significantly enhances the MLLM's multimodal capabilities and task-specific performance. Our instantiation of this approach with VideoChat and LLaVA demonstrates an overall 14.6% improvement in multimodal performance compared to baseline models.
  arXiv  Detail & Related papers  (2024-12-26T18:56:05Z)
• EMMA: Efficient Visual Alignment in Multi-Modal LLMs [56.03417732498859]
  EMMA is a lightweight cross-modality module designed to efficiently fuse visual and textual encodings. EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations.
  arXiv  Detail & Related papers  (2024-10-02T23:00:31Z)
• FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs [5.35588281968644]
  We propose a novel framework, named Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs (Fine CLIPER) Our Fine CLIPER achieves tunable SOTA performance on the DFEW, FERV39k, and MAFW datasets with few parameters.
  arXiv  Detail & Related papers  (2024-07-02T10:55:43Z)
• SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation [61.392147185793476]
  We present a unified and versatile foundation model, namely, SEED-X.<n>SEED-X is able to model multi-granularity visual semantics for comprehension and generation tasks.<n>We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
  arXiv  Detail & Related papers  (2024-04-22T17:56:09Z)
• Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception [63.03288425612792]
  We propose bfAnyRef, a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references. Our model achieves state-of-the-art results across multiple benchmarks, including diverse modality referring segmentation and region-level referring expression generation.
  arXiv  Detail & Related papers  (2024-03-05T13:45:46Z)
• SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models [86.478087039015]
  We present a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. Based on our proposed joint mixing, we propose an efficient strategy aiming to better capture fine-grained appearances of high-resolution images. We hope our work may cast a light on the exploration of joint mixing in future MLLM research.
  arXiv  Detail & Related papers  (2023-11-13T18:59:47Z)
• u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model [17.3535277338312]
  u-LLaVA is an innovative unifying multi-task framework that integrates pixel, regional, and global features to refine the perceptual faculties of MLLMs. This work contributes a novel mask-based multi-task dataset comprising 277K samples, crafted to challenge and assess the fine-grained perception capabilities of MLLMs.
  arXiv  Detail & Related papers  (2023-11-09T13:18:27Z)
• Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification [11.562980171753162]
  Generalizable person re-identification (Re-ID) is a very hot research topic in machine learning and computer vision. Previous methods mainly focus on the visual representation learning, while neglect to explore the potential of semantic features during training. We propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning.
  arXiv  Detail & Related papers  (2023-04-19T08:37:25Z)
• Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning [22.464424641734652]
  Cross-modal alignment is essential for vision-language pre-training models. We propose a novel Semantic Completion Learning task to facilitate global-to-local alignment. We also present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously.
  arXiv  Detail & Related papers  (2022-11-24T06:39:16Z)
• MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning [23.45678557013005]
  We propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover. Our model achieves state-of-the-art performance on various downstream vision-language tasks, including image-text retrieval, visual question answering, visual reasoning, and weakly-supervised visual grounding.
  arXiv  Detail & Related papers  (2022-10-09T06:31:15Z)

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.