A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training

• URL: http://arxiv.org/abs/2608.26095v1
• Date: Wed, 26 Aug 2026 17:57:04 GMT
• Title: A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training
• Abstract summary: Existing unsupervised post-training methods for MLLMs typically optimize target tokens uniformly.<n>Visually Constrained Optimal Transport (VC-OT) formulates the VD structural distortion of old-task VD during new-task learning.<n>Visually Modulated Adaptation (VMA) exploits VD heterogeneity to emphasize visually grounded new-task learning.
• Score: 56.364526893066746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from streaming unlabeled data. Existing unsupervised post-training methods for MLLMs typically optimize target tokens uniformly, overlooking their heterogeneous visual dependence (VD). However, we reveal that token-level VD is crucial for MU-CPT. Specifically, its structural distortion serves as an indicator of cross-modal catastrophic forgetting, and its inherent heterogeneity acts as a compass to guide new-task learning. Leveraging this property, we propose a Visual Dependence-Aware (VDA) framework with two main components. First, Visually Constrained Optimal Transport (VC-OT) formulates the VD structural distortion of old-task VD during new-task learning as an optimal transport problem to mitigate cross-modal forgetting. By designing a region-aware ground cost and a dependence-stratified transport penalty, it prevents global shifts in visual focus while strictly prohibiting visual reliance from degenerating into language bias. Second, Visually Modulated Adaptation (VMA) exploits VD heterogeneity to emphasize visually grounded new-task learning, promoting new-task plasticity. Together, our method simultaneously maintains old-task stability and new-task plasticity during challenging MU-CPT. Extensive experiments under our MU-CPT setting validate the effectiveness of VDA.

Related papers

• UAM: A Dual-Stream Perspective on Forgetting in VLA Training [20.50709311546184]
  Vision--language--action (VLA) models are typically built by fine-tuning a pretrained vision--language model (VLM) on action data.<n>We show that this standard recipe systematically erodes the VLM's multimodal competence.<n>Inspired by the two-stream organization of biological vision, we trace this degradation to a structural bottleneck.
  arXiv  Detail & Related papers  (2026-05-15T08:45:37Z)
• Perception-Aware Multimodal Spatial Reasoning from Monocular Images [57.42071289037214]
  spatial reasoning from monocular images is essential for autonomous driving.<n>Current Vision-Language Models (VLMs) still struggle with fine-grained geometric perception.<n>We propose a perception-aware multimodal reasoning framework that equipsVLMs with explicit object-centric grounding ability.
  arXiv  Detail & Related papers  (2026-03-07T02:05:12Z)
• UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception [71.19234323863314]
  We propose a framework to simultaneously recognize driver behavior, driver emotion, vehicle behavior, and traffic context.<n>Our framework incorporates two core components: dual-branch spatial channel multimodal embedding and adaptive feature-decoupled multi-task loss.<n>We evaluate our method on the AIDE dataset, and the experimental results demonstrate that UV-M3TL achieves state-of-the-art performance across all four tasks.
  arXiv  Detail & Related papers  (2026-02-02T03:35:24Z)
• Learning with Preserving for Continual Multitask Learning [4.847042727427382]
  We introduce Learning with Preserving (LwP), a novel framework that shifts the focus from preserving task outputs to maintaining the shared representation space.<n>LwP not only mitigates catastrophic forgetting but also consistently outperforms state-of-the-art baselines in CMTL tasks.
  arXiv  Detail & Related papers  (2025-11-11T22:23:20Z)
• DAM: Dual Active Learning with Multimodal Foundation Model for Source-Free Domain Adaptation [53.323488295994395]
  Source-free active domain adaptation (SFADA) enhances knowledge transfer from a source model to an unlabeled target domain using limited manual labels selected via active learning.<n>We propose Dual Active learning with Multimodal (DAM) foundation model, a novel framework that integrates multimodal supervision from a ViL model to complement sparse human annotations.<n>Extensive experiments demonstrate that DAM consistently outperforms existing methods and sets a new state-of-the-art across multiple SFADA benchmarks and active learning strategies.
  arXiv  Detail & Related papers  (2025-09-29T15:06:56Z)
• 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)
• Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving [103.745551954983]
  In this paper, we investigate the transfer performance of various types of self-supervised methods, including MoCo and SimCLR, on three downstream tasks. We find that their performances are sub-optimal or even lag far behind the single-task baseline. We propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training.
  arXiv  Detail & Related papers  (2022-09-19T12:15:31Z)

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.