VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

VisCoP (Vision Contextualized Probing) is a novel domain adaptation strategy designed to enhance Large Vision Language Models (VLMs) in new domains without catastrophic forgetting. Existing VLM adaptation methods struggle with distribution shifts, either limiting domain-specific learning or causing knowledge loss. VisCoP addresses this by augmenting a VLM's frozen vision encoder with a compact set of learnable visual probes and interaction modules. These probes extract domain-specific features from intermediate layers via vision-probe cross-attention, feeding them to the LLM. Evaluated across challenging scenarios—cross-view (exocentric to egocentric), cross-modal (RGB to depth), and cross-task (human understanding to robot control)—VisCoP consistently outperforms other strategies. It achieves superior target domain performance, such as a +19.27 gain in depth video understanding and +67.82 in simulated robot control, while better retaining source domain knowledge, even showing positive transfer in some cases.

Key takeaway

For Machine Learning Engineers deploying Vision Language Models in specialized domains, you should consider VisCoP to overcome performance degradation and catastrophic forgetting. This method allows your models to learn domain-specific visual features, like those for egocentric video or robotic control, without overwriting general pre-trained capabilities. By implementing VisCoP's visual probing, you can achieve superior target domain performance while effectively preserving existing knowledge, ensuring robust VLM deployment across diverse applications.

Key insights

VisCoP enables VLMs to adapt to novel visual domains by probing a frozen vision encoder, preventing catastrophic forgetting.

Principles

• Domain adaptation requires balancing new learning with old knowledge retention.
• Probing intermediate layers captures richer domain-specific visual signals.
• Freezing the vision encoder prevents catastrophic forgetting.

Method

VisCoP augments a frozen VLM vision encoder with M=16 learnable visual probes. These probes interact layer-wise with intermediate features via vision-probe cross-attention, then project to the LLM embedding space.

In practice

• Adapt VLMs for egocentric video understanding tasks.
• Enable depth-based perception in VLMs.
• Apply VLMs to robotic control environments.

Topics

• Vision Language Models
• Domain Adaptation
• Catastrophic Forgetting
• Visual Probing
• Video Understanding
• Robotic Control
• Depth Perception

Code references

• dominickrei/VisCoP

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.