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Pre-training image-language transformers for open-vocabulary tasks

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arxiv 2209.04372 v1 pith:23XRBAGG submitted 2022-09-09 cs.CV

classification cs.CV
keywords pre-trainingtaskscaptioninglanguagevisionvisualadditionalanswering
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a pre-training approach for vision and language transformer models, which is based on a mixture of diverse tasks. We explore both the use of image-text captioning data in pre-training, which does not need additional supervision, as well as object-aware strategies to pre-train the model. We evaluate the method on a number of textgenerative vision+language tasks, such as Visual Question Answering, visual entailment and captioning, and demonstrate large gains over standard pre-training methods.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Language-Instructed Vision Embeddings for Controllable and Generalizable Perception

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    LIVE uses language to generate task-centric vision embeddings at inference, reducing hallucinations by 34 points on MMVP, outperforming larger VLMs on VQA, and generalizing to unseen tasks.

  2. PaliGemma 2: A Family of Versatile VLMs for Transfer

    cs.CV 2024-12 unverdicted novelty 4.0 of 10

    PaliGemma 2 is a family of vision-language models that achieves state-of-the-art results on transfer tasks like table structure recognition and radiography report generation by combining SigLIP with Gemma 2 models at ...

  3. PaliGemma: A versatile 3B VLM for transfer

    cs.CV 2024-07 unverdicted novelty 4.0 of 10

    PaliGemma is an open 3B VLM based on SigLIP and Gemma that achieves strong performance on nearly 40 diverse open-world tasks including benchmarks, remote-sensing, and segmentation.

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