Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise

Leverage robust visual information to stabilize prompt learning in vision-language models. This cross-modal approach mitigates the impact of label noise, improving model performance and reliability with imperfect datasets.

intermediate30 min5 steps

The play

Recognize Prompt Vulnerability
Understand that traditional prompt learning in Vision-Language Models (VLMs) is highly susceptible to label noise, which can degrade model performance.
Prioritize Visual Robustness
Acknowledge that visual content inherently provides more reliable and robust semantic information compared to potentially noisy text prompts or labels.
Design Visual Guidance Mechanism
Integrate a strategy into your VLM training pipeline that uses visual features to guide, regularize, or stabilize the prompt learning process. This could involve modifying loss functions or architectural components.
Evaluate Under Noise Conditions
Rigorously test your vision-guided prompt learning model's performance and robustness specifically in environments with varying levels of label noise to confirm its effectiveness.
Deploy with Imperfect Data
Apply this enhanced, robust VLM approach to real-world datasets known to have inconsistent or imperfect labels, reducing the dependency on perfectly clean, labor-intensive annotations.

Starter code

training:
  optimizer: Adam
  learning_rate: 0.001
  epochs: 10
  loss_function: CrossEntropyLoss
  vision_guided_prompt_learning:
    enabled: true
    guidance_type: "feature_alignment" # e.g., 'contrastive', 'consistency', 'regularization'
    guidance_weight: 0.1
    visual_feature_source: "vision_encoder_output"
    prompt_feature_source: "learned_prompt_embedding"
    temperature: 0.07 # For contrastive guidance

Source

Paperarxiv.org