Innovative Framework

Exploring cross-modal learning through advanced analysis, training, and transfer techniques for robust performance.

A person rides a bicycle across a street while a pedestrian crosswalk is visible in the foreground. The background features a dark building facade with signage. The lighting casts shadows on the road, creating a contrast between the bright crosswalk and the darker surroundings.

A group of people walking across a pedestrian crossing in an urban street setting. Some individuals are carrying shopping bags. Buildings are visible in the background with various signs, some with Asian characters and one advertising crispy milk donuts. The scene is in black and white, emphasizing contrasts and shadows.

Phase One

Conduct feature space analysis to quantify alignment between vision and language embeddings effectively.

Phase Two

Implement disentangled fusion training to enhance modality invariance and improve cross-domain representation learning.

A black-and-white urban scene with people crossing a pedestrian crossing. Two people are pushing bicycles, and one person is cycling across. The background features city buildings and a quiet street, suggesting a typical city environment.

Phase One Analysis

Phase one focuses on feature space analysis and alignment quantification using advanced probing techniques for vision and language embeddings in models like CLIP.

A workspace featuring a tablet and a laptop on a wooden desk. The tablet displays an online learning platform with various course thumbnails, while the laptop shows a coding environment with a colorful code editor. The setup suggests a focus on learning and coding.

Phase Two Training

Phase two emphasizes disentangled fusion training through adversarial decoders and contrastive learning to enhance cross-domain representation accuracy and invariance.

Framework

Innovative analysis and training for cross-modal learning.