newsReddit r/MachineLearningTrust 52 · CommunityPublished 3d agoLive · 2d ago

I reduced image-processing token usage by ~95% compared with GPT-4o direct vision, while maintaining roughly the same accuracy.How significant is that?[P]

I'm testing a new approach for reducing the cost of image-based LLM inference. I evaluated it on the MOMA Graph benchmark , using 1,315 questions . Compared with using GPT-4o to process the original images directly, I observed approximately: ~95% lower token usage roughly the same accuracy as the GPT-4o direct-image baseline I'm intentionally not sharing

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PossiblePossibly related (embedding) · 51%SigLIP-HD by Fine-to-Coarse Supervision →
PossiblePossibly related (embedding) · 49%kyegomez/VisionMamba →
PossiblePossibly related (embedding) · 47%Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs →
PossiblePossibly related (embedding) · 47%VisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression →
PossiblePossibly related (embedding) · 46%zwmaronek/Beyond-Early-Exit →

Covers

paperSigLIP-HD by Fine-to-Coarse Supervision repokyegomez/VisionMamba paperLook Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs paperVisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression repozwmaronek/Beyond-Early-Exit

Related across the graph

paperLook Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs paperSigLIP-HD by Fine-to-Coarse Supervision repozwmaronek/Beyond-Early-Exit repokyegomez/VisionMamba paperVisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression