Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2504.16516v2
• Date: Thu, 24 Apr 2025 19:36:09 GMT
• Title: Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation
• Authors: Junrong Yue, Yifan Zhang, Chuan Qin, Bo Li, Xiaomin Lie, Xinlei Yu, Wenxin Zhang, Zhendong Zhao,
• Abstract summary: Vision-and-Language Navigation (VLN) aims to enable embodied agents to follow natural language instructions and reach target locations in real-world environments.<n>We propose a Multi-level Fusion and Reasoning Architecture (MFRA) to enhance the agent's ability to reason over visual observations, language instructions and navigation history.
• Score: 11.23342183103283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-and-Language Navigation (VLN) aims to enable embodied agents to follow natural language instructions and reach target locations in real-world environments. While prior methods often rely on either global scene representations or object-level features, these approaches are insufficient for capturing the complex interactions across modalities required for accurate navigation. In this paper, we propose a Multi-level Fusion and Reasoning Architecture (MFRA) to enhance the agent's ability to reason over visual observations, language instructions and navigation history. Specifically, MFRA introduces a hierarchical fusion mechanism that aggregates multi-level features-ranging from low-level visual cues to high-level semantic concepts-across multiple modalities. We further design a reasoning module that leverages fused representations to infer navigation actions through instruction-guided attention and dynamic context integration. By selectively capturing and combining relevant visual, linguistic, and temporal signals, MFRA improves decision-making accuracy in complex navigation scenarios. Extensive experiments on benchmark VLN datasets including REVERIE, R2R, and SOON demonstrate that MFRA achieves superior performance compared to state-of-the-art methods, validating the effectiveness of multi-level modal fusion for embodied navigation.

Related papers

• IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification [60.38841251693781]
  We propose a novel framework to generate robust multi-modal object ReIDs.<n>Our framework uses Modal Prefixes and InverseNet to integrate multi-modal information with semantic guidance from inverted text.<n>Experiments on three multi-modal object ReID benchmarks demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2025-03-13T13:00:31Z)
• Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment [21.36633828492347]
  Cross-Domain Multi-Modal Few-Shot Object Detection (CDMM-FSOD)<n>We introduce a meta-learning-based framework designed to leverage rich textual semantics as an auxiliary modality to achieve effective domain adaptation.<n>We evaluate the proposed method on common cross-domain object detection benchmarks and demonstrate that it significantly surpasses existing few-shot object detection approaches.
  arXiv  Detail & Related papers  (2025-02-23T06:59:22Z)
• Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation [35.71602601385161]
  We present a novel vision-language model (VLM)-based navigation framework.<n>Our approach enhances spatial reasoning and decision-making in long-horizon tasks.<n> Experimental results demonstrate that the proposed method surpasses previous state-of-the-art approaches in object navigation tasks.
  arXiv  Detail & Related papers  (2025-02-20T04:41:40Z)
• OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation [96.46961207887722]
  OVER-NAV aims to go over and beyond the current arts of IVLN techniques. To fully exploit the interpreted navigation data, we introduce a structured representation, coded Omnigraph.
  arXiv  Detail & Related papers  (2024-03-26T02:34:48Z)
• Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions [126.3136109870403]
  We introduce a generic and lightweight Visual Prompt Generator Complete module (VPG-C) VPG-C infers and completes the missing details essential for comprehending demonstrative instructions. We build DEMON, a comprehensive benchmark for demonstrative instruction understanding.
  arXiv  Detail & Related papers  (2023-08-08T09:32:43Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation [3.809880620207714]
  Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems.
  arXiv  Detail & Related papers  (2023-05-05T15:06:08Z)
• BEVBert: Multimodal Map Pre-training for Language-guided Navigation [75.23388288113817]
  We propose a new map-based pre-training paradigm that is spatial-aware for use in vision-and-language navigation (VLN) We build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map. Based on the hybrid map, we devise a pre-training framework to learn a multimodal map representation, which enhances spatial-aware cross-modal reasoning thereby facilitating the language-guided navigation goal.
  arXiv  Detail & Related papers  (2022-12-08T16:27:54Z)
• Self-paced Multi-grained Cross-modal Interaction Modeling for Referring Expression Comprehension [21.000045864213327]
  referring expression comprehension (REC) generally requires a large amount of multi-grained information of visual and linguistic modalities to realize accurate reasoning. How to aggregate multi-grained information from different modalities and extract abundant knowledge from hard examples is crucial in the REC task. We propose a Self-paced Multi-grained Cross-modal Interaction Modeling framework, which improves the language-to-vision localization ability.
  arXiv  Detail & Related papers  (2022-04-21T08:32:47Z)
• Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentation [87.01669173673288]
  We propose an encoder fusion network (EFN), which transforms the visual encoder into a multi-modal feature learning network. A co-attention mechanism is embedded in the EFN to realize the parallel update of multi-modal features. The experiment results on four benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-05-05T02:27:25Z)
• Global Context-Aware Progressive Aggregation Network for Salient Object Detection [117.943116761278]
  We propose a novel network named GCPANet to integrate low-level appearance features, high-level semantic features, and global context features. We show that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-03-02T04:26:10Z)

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.