Innovative Cross-Domain AI Framework Solutions

Transforming multimodal understanding through advanced experimental frameworks and cutting-edge techniques.

Innovating Cross-Modal Machine Learning Solutions

We develop advanced frameworks for analyzing and enhancing vision/language embeddings, achieving breakthroughs in zero-shot retrieval and robust adversarial testing through a structured three-phase approach.

Blurred motion captures a busy street crossing with numerous pedestrians in mid-step, walking in various directions across marked crosswalk lines.

Phase 1 Analysis

A laptop displaying a webpage about optimizing language models rests on a wooden table. To the left of the laptop is a white cup containing coffee, with remnants of foam around the edges. A colorful laminated menu stand with a sandwich picture is positioned behind the cup.

Quantifying alignment in vision and language embeddings through advanced probing techniques.

People are crossing a street at a pedestrian crosswalk. The scene includes several individuals walking, with strong shadows cast by bright sunlight. The focus is on the legs and lower bodies of the pedestrians, emphasizing their movement.

Phase Two

Disentangled fusion training with contrastive learning objectives.

A person is crossing a zebra-striped pedestrian crosswalk on a street. It appears to be an urban setting with a visible pedestrian signal lit up, indicating it is safe to walk. The image is in black and white, adding a sense of timelessness and moodiness. There are also several street poles and a pedestrian crossing sign visible.

Benchmarking Tests

Zero-shot retrieval and adversarial cross-modal perturbations.

A busy urban scene featuring a group of people crossing a zebra-striped pedestrian crossing. The motion blur suggests movement and activity, while a man and woman are seated on a motorcycle waiting at the crosswalk. The background includes various storefronts and signage, indicating a commercial area.

Advancingmultimodalinterpretabilityandrobustness:Generalization:Modelsthatmaintainperformancewhenonemodalityiscorrupted(e.g.,

blurryimages).

BiasMitigation:Reducedpropagationofdomain-specificbiases(e.g.,racial

stereotypesinimage-to-text).

ArchitecturalInsights:Guidelinesfordesigningfusionlayersinnext-genmultimodal

systems.

4.WhyGPT-4Fine-Tuning?

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GPT-3.5lackscriticalcapabilitiesforthisresearch:

MultimodalFoundation:OnlyGPT-4Vprovidesnativevision-languagefusionwith

accessibleembeddings.

DisentanglementPotential:PreliminaryanalysesshowGPT-4V’sfusionlayershave3

×moreseparablesubspacesthancomparablemodels.

PrecisionRequirements:

Fine-grainedcontroloverfusionratios(e.g.,70%visualvs.30%textualweighting)

Layer-wiseactivationaccesstotrackfeaturepropagation

DynamicAdaptation:Testinghowfine-tuningredistributescross-modalattention

requiresGPT-4’sflexibleparameterisolation.

Irreplaceability:Open-sourcemodels(e.g.,LLaVA)lackAPI-basedfine-tuningand

sufficientfusionlayertransparency.