#
Multimodal Text Generation (BLIP)

##
Introduction

After the release of CLIP, the AI community recognized the immense potential of larger datasets and contrastive learning in deep learning. One significant development in this area is BLIP (Bootstrapping Language-Image Pre-training), which extends the capabilities of multimodal models to include text generation.

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CapFilt: Caption and Filtering

As multimodal models require large datasets, they often have to be scraped from the internet using image and alternative-text (alt-text) pairs. However, the alt-texts often do not accurately describe the visual content of the images, making them a noisy signal that is suboptimal for learning vision-language alignment. Hence, the BLIP paper introduced a caption and filtering mechanism (CapFilt). This is made up of a deep learning model which filters out noisy pairs and another model which creates captions for images. Both of these models are first fine-tuned using a human annotated dataset. They found that cleaning the dataset using CapFit produced superior performance to just using the web dataset. Further details on this process can be found in the BLIP paper.

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BLIP Architecture and Training

The BLIP architecture combines a vision encoder and a Multimodal Mixture of Encoder-Decoder (MED), enabling versatile processing of both visual and textual data. Its structure is shown in the figure below which features (blocks with the same color share parameters):

- Vision Transformer (ViT): This is a plain vision transformer featuring self-attention, feed-forward blocks, and a [CLS] token for embedding representation.
- Unimodal Text Encoder: Resembling BERT’s architecture, it uses a [CLS] token for embedding and employs contrastive loss like CLIP, for aligning image and text representations.
- Image-Grounded Text Encoder: This substitutes the [CLS] token with an [Encode] token. Cross-attention layers enable the integration of image and text embeddings, creating a multimodal representation. It employs a linear layer to assess the congruence of image-text pairs.
- Image-Grounded Text Decoder: Replacing the bidirectional self-attention with causal self-attention, this decoder is trained via cross-entropy loss in an autoregressive manner for tasks like caption generation or answering visual questions.

BLIP’s architecture integrates a vision encoder with a multimodal mixture of encoder-decoder components, enabling advanced text and image processing. This design allows it to adeptly handle diverse tasks, from aligning image-text pairs to generating captions and answering visual questions.

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Example Use Case: BLIP-2

Following BLIP’s success, it’s creator Salesforce introduced BLIP-2, an enhanced iteration. BLIP-2’s advancements and capabilities are detailed in the BLIP-2 paper and the Hugging Face documentation. Here, we utilize BLIP-2 for visual questioning answering.

    from PIL import Image
    import requests
    from transformers import Blip2Processor, Blip2ForConditionalGeneration
    import torch

    device = "cuda" if torch.cuda.is_available() else "cpu"

    processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
    model = Blip2ForConditionalGeneration.from_pretrained(
        "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
    )
    model.to(device)
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)

    prompt = "Question: How many remotes are there? Answer:"
    inputs = processor(images=image, text=prompt, return_tensors="pt").to(
        device, torch.float16
    )
    outputs = model.generate(**inputs)
    text = processor.tokenizer.batch_decode(outputs, skip_special_tokens=True)
    print(text)

This code snippet illustrates the application of BLIP-2 for visual question answering. Experiment with more complex queries or explore this functionality further using the provided Gradio app:

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Conclusion

BLIP marks a significant milestone in multimodal text generation, leveraging CLIP’s strengths to create a robust model. It underscores the importance of dataset quality over quantity, contributing to the advancement of multimodal learning. For more details, refer to the BLIP paper, BLIP-2 paper, and the Hugging Face documentation for BLIP and BLIP-2.

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