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PaliGemma: A versatile 3B VLM for transfer

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PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation.

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  • abstract PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation.

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Koshur Pixel: a large-scale synthetic ocr dataset for kashmiri

cs.CV · 2026-06-22 · unverdicted · novelty 7.0

Koshur Pixel is the first large-scale synthetic OCR dataset for Kashmiri with 613,078 image-text pairs generated via SynthOCR-Gen from the KS-PRET-5M corpus across multiple fonts and granularities with 25+ augmentations.

DSCA: Dynamic Subspace Concept Alignment for Lifelong VLM Editing

cs.CV · 2026-04-09 · unverdicted · novelty 7.0

DSCA turns concept isolation into an architectural property by dynamically creating orthogonal subspaces for non-interfering lifelong edits in vision-language models, sustaining over 95% success after 1000 sequential edits.

SAM 3: Segment Anything with Concepts

cs.CV · 2025-11-20 · unverdicted · novelty 7.0

SAM 3 introduces promptable concept segmentation that doubles accuracy of prior systems on images and videos while improving standard SAM segmentation performance.

TextTeacher: What Can Language Teach About Images?

cs.CV · 2026-05-21 · unverdicted · novelty 6.0

TextTeacher uses frozen text embeddings from captions as semantic anchors to guide vision model training, improving ImageNet accuracy by up to 2.7 p.p. and transfer performance by 1.0 p.p. on average.

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