MANTA: Cross-Modal Semantic Alignment and Information-Theoretic Optimization for Long-form Multimodal Understanding

• URL: http://arxiv.org/abs/2507.00068v1
• Date: Sat, 28 Jun 2025 12:12:06 GMT
• Title: MANTA: Cross-Modal Semantic Alignment and Information-Theoretic Optimization for Long-form Multimodal Understanding
• Authors: Ziqi Zhong, Daniel Tang,
• Abstract summary: MANTA is a framework that unifies visual and auditory inputs into a structured textual space for seamless processing with large language models.<n>MANTA addresses four key challenges: (1) semantic alignment across modalities with information-theoretic optimization, (2) adaptive temporal synchronization for varying information densities, and (3) hierarchical content representation for multi-scale understanding.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While multi-modal learning has advanced significantly, current approaches often treat modalities separately, creating inconsistencies in representation and reasoning. We introduce MANTA (Multi-modal Abstraction and Normalization via Textual Alignment), a theoretically-grounded framework that unifies visual and auditory inputs into a structured textual space for seamless processing with large language models. MANTA addresses four key challenges: (1) semantic alignment across modalities with information-theoretic optimization, (2) adaptive temporal synchronization for varying information densities, (3) hierarchical content representation for multi-scale understanding, and (4) context-aware retrieval of sparse information from long sequences. We formalize our approach within a rigorous mathematical framework, proving its optimality for context selection under token constraints. Extensive experiments on the challenging task of Long Video Question Answering show that MANTA improves state-of-the-art models by up to 22.6% in overall accuracy, with particularly significant gains (27.3%) on videos exceeding 30 minutes. Additionally, we demonstrate MANTA's superiority on temporal reasoning tasks (23.8% improvement) and cross-modal understanding (25.1% improvement). Our framework introduces novel density estimation techniques for redundancy minimization while preserving rare signals, establishing new foundations for unifying multimodal representations through structured text.

Related papers

• FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation [50.438552588818]
  We propose textbfFindRec (textbfFlexible unified textbfinformation textbfdisentanglement for multi-modal sequential textbfRecommendation)<n>A Stein kernel-based Integrated Information Coordination Module (IICM) theoretically guarantees distribution consistency between multimodal features and ID streams.<n>A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance.
  arXiv  Detail & Related papers  (2025-07-07T04:09:45Z)
• MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings [75.0617088717528]
  MoCa is a framework for transforming pre-trained VLM backbones into effective bidirectional embedding models.<n>MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-29T06:41:00Z)
• MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models [18.73221445082855]
  Multimodal Large Language Models (MLLMs) have demonstrated significant advances across numerous vision-language tasks.<n>We introduce MUCAR, a novel benchmark designed explicitly for evaluating multimodal ambiguity resolution across multilingual and cross-modal scenarios.
  arXiv  Detail & Related papers  (2025-06-20T14:57:41Z)
• Contra4: Evaluating Contrastive Cross-Modal Reasoning in Audio, Video, Image, and 3D [107.69104331520677]
  We introduce Contra4, a dataset for contrastive cross-modal reasoning across four modalities: image, audio, video, and 3D.<n> Contra4 combines human-annotated captions with a mixture-of-models filter to ensure high-quality supervision, resulting in 174k training examples and a manually verified test set of 2.3k samples.<n>While task-specific fine-tuning improves performance by 56% relative to baseline, state-of-the-art models still achieve only 56% accuracy overall and 42% in four-modality settings.
  arXiv  Detail & Related papers  (2025-06-02T03:12:13Z)
• Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment [0.0]
  We propose UMaT, a framework that unifies visual and auditory inputs as structured text for large language models.<n>It significantly improves state-of-the-art Long Video Question Answering accuracy.
  arXiv  Detail & Related papers  (2025-03-12T05:28:24Z)
• Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning [22.54577327204281]
  Multimodal sentiment analysis aims to learn representations from different modalities to identify human emotions. Existing works often neglect the frame-level redundancy inherent in continuous time series, resulting in incomplete modality representations with noise. We propose temporal-invariant learning for the first time, which constrains the distributional variations over time steps to effectively capture long-term temporal dynamics.
  arXiv  Detail & Related papers  (2024-08-30T03:28:40Z)
• Enhancing Multimodal Unified Representations for Cross Modal Generalization [52.16653133604068]
  We propose Training-free Optimization of Codebook (TOC) and Fine and Coarse cross-modal Information Disentangling (FCID)<n>These methods refine the unified discrete representations from pretraining and perform fine- and coarse-grained information disentanglement tailored to the specific characteristics of each modality.
  arXiv  Detail & Related papers  (2024-03-08T09:16:47Z)
• X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning [109.9413329636322]
  This paper introduces an efficient framework that integrates multiple modalities (images, 3D, audio and video) to a frozen Large Language Models (LLMs) Our approach explores two distinct projection mechanisms: Q-Formers and Linear Projections (LPs)
  arXiv  Detail & Related papers  (2023-11-30T18:43:51Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Robust Latent Representations via Cross-Modal Translation and Alignment [36.67937514793215]
  Most multi-modal machine learning methods require that all the modalities used for training are also available for testing. To address this limitation, we aim to improve the testing performance of uni-modal systems using multiple modalities during training only. The proposed multi-modal training framework uses cross-modal translation and correlation-based latent space alignment.
  arXiv  Detail & Related papers  (2020-11-03T11:18:04Z)

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.