Innovative Framework

Exploring cross-modal alignment and robust transfer through advanced neural network methodologies and training techniques.

A blurred black and white scene featuring multiple people in motion, possibly crossing a street. A large tree is visible in the background and the words 'LOOK RIGHT' are painted on the pavement.

Phase One

Features space analysis using orthogonal probing and gradient-based attribution to understand cross-modal and unimodal decisions in CLIP-style models.

A cyclist is riding across a pedestrian crosswalk on a city street. The scene is in black and white, with vehicles visible in the background. Shadows are cast dramatically across the scene, suggesting evening or early morning light.

Phase Two

Disentangled fusion training with adversarial decoders and contrastive learning objectives to enhance modality invariance and similarity across domains.

Framework

Innovative analysis and training for cross-modal learning.