Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation

• URL: http://arxiv.org/abs/2506.15757v1
• Date: Wed, 18 Jun 2025 11:43:50 GMT
• Title: Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation
• Authors: Ruoyu Wang, Tong Yu, Junda Wu, Yao Liu, Julian McAuley, Lina Yao,
• Abstract summary: Visual Language Navigation (VLN) is a fundamental task within the field of Embodied AI, focusing on the ability of agents to navigate complex environments based on natural language instructions.<n>Existing methods rely on pre-trained backbone models for visual perception, which struggle with the dynamic viewpoints in VLN scenarios.<n>We propose Weakly-supervised Partial Contrastive Learning (WPCL), a method that enhances an agent's ability to identify objects from dynamic viewpoints in VLN scenarios without requiring VLM fine-tuning.
• Score: 36.17444261325021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Language Navigation (VLN) is a fundamental task within the field of Embodied AI, focusing on the ability of agents to navigate complex environments based on natural language instructions. Despite the progress made by existing methods, these methods often present some common challenges. First, they rely on pre-trained backbone models for visual perception, which struggle with the dynamic viewpoints in VLN scenarios. Second, the performance is limited when using pre-trained LLMs or VLMs without fine-tuning, due to the absence of VLN domain knowledge. Third, while fine-tuning LLMs and VLMs can improve results, their computational costs are higher than those without fine-tuning. To address these limitations, we propose Weakly-supervised Partial Contrastive Learning (WPCL), a method that enhances an agent's ability to identify objects from dynamic viewpoints in VLN scenarios by effectively integrating pre-trained VLM knowledge into the perception process, without requiring VLM fine-tuning. Our method enhances the agent's ability to interpret and respond to environmental cues while ensuring computational efficiency. Experimental results have shown that our method outperforms the baseline methods on multiple benchmarks, which validate the effectiveness, robustness and generalizability of our method.

Related papers

• VLM Can Be a Good Assistant: Enhancing Embodied Visual Tracking with Self-Improving Vision-Language Models [34.60772103760521]
  We introduce a novel framework that enhances Embodied Visual Tracking (EVT) with Vision-Language Models (VLMs)<n>This work represents the first integration of VLM-based reasoning to assist EVT agents in proactive failure recovery.
  arXiv  Detail & Related papers  (2025-05-27T04:53:50Z)
• EMAC+: Embodied Multimodal Agent for Collaborative Planning with VLM+LLM [8.3321872381107]
  We introduce EMAC+, an Embodied Multimodal Agent that collaboratively integrates LLM and VLM.<n>Unlike existing methods, EMAC+ dynamically refines high-level textual plans using real-time feedback from a VLM executing low-level visual control tasks.<n>EMAC+ achieves superior task performance, against noisy observations, and efficient learning.
  arXiv  Detail & Related papers  (2025-05-26T12:34:16Z)
• Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning [17.59802090014789]
  We introduce PrefVLM, a framework that integrates Vision-Language Models (VLMs) with selective human feedback.<n>Our method leverages VLMs to generate initial preference labels, which are then filtered to identify uncertain cases for targeted human annotation.<n> Experiments on Meta-World manipulation tasks demonstrate that PrefVLM achieves comparable or superior success rates to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-02-03T18:50:15Z)
• OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation [95.78870389271832]
  The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision.<n>We propose OLA-VLM, the first approach distilling knowledge into the LLM's hidden representations from a set of target visual representations.<n>We show that OLA-VLM boosts performance by an average margin of up to 2.5% on various benchmarks, with a notable improvement of 8.7% on the Depth task in CV-Bench.
  arXiv  Detail & Related papers  (2024-12-12T18:55:18Z)
• Improving Generalization in Visual Reasoning via Self-Ensemble [0.0]
  We propose self-ensemble, a novel method that improves the generalization and visual reasoning of the model without updating any parameters. Our key insight is that LVLM itself can ensemble without the need for any other LVLMs, which helps to unlock their internal capabilities.
  arXiv  Detail & Related papers  (2024-10-28T10:04:40Z)
• EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty.<n>We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.<n>Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning [79.38140606606126]
  We propose an algorithmic framework that fine-tunes vision-language models (VLMs) with reinforcement learning (RL) Our framework provides a task description and then prompts the VLM to generate chain-of-thought (CoT) reasoning. We demonstrate that our proposed framework enhances the decision-making capabilities of VLM agents across various tasks.
  arXiv  Detail & Related papers  (2024-05-16T17:50:19Z)
• Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight [20.92646531472541]
  We propose a novel approach that combines the performance of Reinforcement Learning (RL) and the sample efficiency of Imitation Learning (IL) Our framework contains three phases teacher policy using RL with privileged state information distilling it into a student policy via IL, and adaptive fine-tuning via RL. Tests show our approach can not only learn in scenarios where RL from scratch fails but also outperforms existing IL methods in both robustness and performance.
  arXiv  Detail & Related papers  (2024-03-18T19:25:57Z)
• Vision-Language Models Provide Promptable Representations for Reinforcement Learning [67.40524195671479]
  We propose a novel approach that uses the vast amounts of general and indexable world knowledge encoded in vision-language models (VLMs) pre-trained on Internet-scale data for embodied reinforcement learning (RL) We show that our approach can use chain-of-thought prompting to produce representations of common-sense semantic reasoning, improving policy performance in novel scenes by 1.5 times.
  arXiv  Detail & Related papers  (2024-02-05T00:48:56Z)
• Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning [67.0609518552321]
  We propose to conduct Machine Vision Therapy which aims to rectify the noisy predictions from vision models. By fine-tuning with the denoised labels, the learning model performance can be boosted in an unsupervised manner.
  arXiv  Detail & Related papers  (2023-12-05T07:29:14Z)
• Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models [61.28463542324576]
  Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can generate human-like outputs. We evaluate existing state-of-the-art VLMs and find that even the best-performing model is unable to demonstrate strong visual reasoning capabilities and consistency. We propose a two-stage training framework aimed at improving both the reasoning performance and consistency of VLMs.
  arXiv  Detail & Related papers  (2023-09-08T17:49:44Z)

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.