Continual Learning with Vision-Language Models via Semantic-Geometry Preservation | ScienceToStartup | ScienceToStartup

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References (65)

[1]

Evaluating the Adversarial Robustness of Vision-Language Models via Internal Feature Perturbations

2026Chaohu Liu, Yubo Wang et al.

[2]

Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning

2025Tao Hu, Lan Li et al.

[3]

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

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"SeGP-CL enhances continual learning in vision-language models by preserving semantic geometry to prevent catastrophic forgetting."

Continual Learning•Score: 4•View PDF ↗

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation

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References (65)

Founder's Pitch

"SeGP-CL enhances continual learning in vision-language models by preserving semantic geometry to prevent catastrophic forgetting."

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation

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References (65)

Founder's Pitch

"SeGP-CL enhances continual learning in vision-language models by preserving semantic geometry to prevent catastrophic forgetting."

Commercial Viability Breakdown

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