A recent paper titled “Image Captioners Are Scalable Vision Learners Too” presents an intriguing approach called CapPa, which goals to determine image captioning as a competitive pre-training strategy for vision backbones. The paper, authored by a DeepMind research team, highlights the potential of CapPa to rival the impressive performance of Contrastive Language Image Pretraining (CLIP) while offering simplicity, scalability, and efficiency.

The researchers extensively compared Cap, their image captioning strategy, and the widely popular CLIP approach. They rigorously matched the pretraining compute, model capability, and training data between the 2 strategies to make sure a good evaluation. The researchers found that Cap vision backbones outperformed CLIP models across several tasks, including few-shot classification, captioning, optical character recognition (OCR), and visual query answering (VQA). Furthermore, when transferring to classification tasks with large labeled training data, Cap vision backbones achieved comparable performance to CLIP, indicating their potential superiority in multimodal downstream tasks.

To further enhance the performance of Cap, the researchers introduced the CapPa pretraining procedure, which mixes autoregressive prediction (Cap) with parallel prediction (Pa). They employed Vision Transformer (ViT) because the vision encoder, leveraging its strong capabilities in image understanding. For predicting image captions, the researchers utilized a typical Transformer decoder architecture, incorporating cross-attention to make use of the ViT-encoded sequence within the decoding process effectively.

As a substitute of solely training the model in an autoregressive way within the training stage, the researchers adopted a parallel prediction approach where the model predicts all caption tokens independently and concurrently. By doing so, the decoder can heavily depend on image information to enhance prediction accuracy, because it has access to the total set of tokens in parallel. This strategy allows the decoder to learn from the wealthy visual context provided by the image.

The researchers conducted a study to judge the performance of CapPa compared to traditional Cap and the state-of-the-art CLIP approach across a wide selection of downstream tasks, including image classification, captioning, OCR, and VQA. The outcomes were highly promising, as CapPa consistently outperformed Cap on just about all tasks. Moreover, in comparison with CLIP* trained with the identical batch size, CapPa achieved comparable or superior performance. Moreover, CapPa showcased strong zero-shot capabilities, enabling effective generalization to unseen tasks, and exhibited promising scaling properties, indicating its potential to handle larger-scale datasets and models.

Overall, the work presented within the paper establishes image captioning as a competitive pre-training strategy for vision backbones. By showcasing the effectiveness of CapPa in achieving high-quality results across various downstream tasks, the research team hopes to encourage further exploration of captioning as a pre-training task for vision encoders. With its simplicity, scalability, and efficiency, CapPa opens up exciting possibilities for advancing vision-based models and pushing the boundaries of multimodal learning.

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