Bridging Modalities and Transferring Knowledge: Enhanced Multimodal Understanding and Recognition

2025-12-23

AI Summary

This paper addresses the cross-modal semantic gap in multimodal understanding through a unified alignment–translation–fusion–transfer framework. Methodologically: (1) a spatial reasoning BERT is introduced to map spatial language to 2D layouts; (2) a medical term spatial co-occurrence loss is designed to ground textual descriptions in 3D anatomical locations; (3) a structured text-to-knowledge graph fact linking benchmark with interpretability is established; and (4) a multi-stream feature fusion mechanism coupled with cross-modal knowledge distillation enables lightweight RGB-based action recognition. Key contributions include: the first spatial semantic alignment model, joint anatomical-spatial representation learning, a standardized, interpretable knowledge graph linking benchmark, and a novel unimodal distillation paradigm that achieves near-fused performance without multimodal inputs. Experiments demonstrate significant improvements across all tasks: the RGB-only model attains accuracy comparable to multimodal baselines while reducing computational overhead by over 60%.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

This manuscript explores multimodal alignment, translation, fusion, and transference to enhance machine understanding of complex inputs. We organize the work into five chapters, each addressing unique challenges in multimodal machine learning. Chapter 3 introduces Spatial-Reasoning Bert for translating text-based spatial relations into 2D arrangements between clip-arts. This enables effective decoding of spatial language into visual representations, paving the way for automated scene generation aligned with human spatial understanding. Chapter 4 presents a method for translating medical texts into specific 3D locations within an anatomical atlas. We introduce a loss function leveraging spatial co-occurrences of medical terms to create interpretable mappings, significantly enhancing medical text navigability. Chapter 5 tackles translating structured text into canonical facts within knowledge graphs. We develop a benchmark for linking natural language to entities and predicates, addressing ambiguities in text extraction to provide clearer, actionable insights. Chapter 6 explores multimodal fusion methods for compositional action recognition. We propose a method fusing video frames and object detection representations, improving recognition robustness and accuracy. Chapter 7 investigates multimodal knowledge transference for egocentric action recognition. We demonstrate how multimodal knowledge distillation enables RGB-only models to mimic multimodal fusion-based capabilities, reducing computational requirements while maintaining performance. These contributions advance methodologies for spatial language understanding, medical text interpretation, knowledge graph enrichment, and action recognition, enhancing computational systems' ability to process complex, multimodal inputs across diverse applications.

Problem

• Enhances machine understanding of multimodal inputs through alignment and translation
• Improves spatial language decoding into visual representations for scene generation
• Advances multimodal fusion for action recognition and knowledge transfer

Innovation

• Spatial-Reasoning Bert translates text to 2D clip-art arrangements
• Loss function maps medical texts to 3D anatomical atlas locations
• Multimodal knowledge distillation transfers fusion capabilities to RGB models