we generate captions to the images which are given by user(user input) using prompt engineering and Generative AI
We use Zero-shot prompting technique in developing the prompt to generate image captions
Vision models can look at pictures and then tell you what's in them using words. These are called vision-to-text models. They bring together the power of understanding images and language. Using fancy neural networks, these models can look at pictures and describe them in a way that makes sense. They're like a bridge between what you see and what you can read.
This is super useful for things like making captions for images, helping people who can't see well understand what's in a picture, and organizing information. As these models get even smarter, they're going to make computers even better at understanding and talking about what they "see" in pictures. It's like teaching computers to understand and describe the visual world around us.
Based on which model we are using like OPENAI,GEMINI(sub models),Anthropic we use respective API KEY and also we use endpoint if necessary
➡️Generative model (predefined function) may play a key role in the whole process of development.
Evidence suggests Gemini represents the state-of-the-art in foundation models:
It achieves record-breaking results on over 56 benchmarks spanning text, code, math, visual, and audio understanding. This includes benchmarks like MMLU, GSM8K, MATH, Big-Bench Hard, HumanEval, Natural2Code, DROP, and WMT23.
➡️Notably, Gemini Ultra is the first to achieve human-expert performance on MMLU across 57 subjects with scores above 90%.
➡️On conceptual reasoning benchmarks like BIG-Bench, Gemini outperforms expert humans in areas like math, physics, and CS.
➡️Specialized versions create state-of-the-art applications like the code generator AlphaCode 2 which solves programming problems better than 85% of human coders in competitions.
➡️Qualitative examples show Gemini can manipulate complex math symbols, correct errors in derivations, generate relevant UIs based on conversational context, and more.
Google does not disclose details of Gemini architecture. But some multi-modal architectures shared by research community give us some intuitions on how it might work.
The CLIP architecture, introduced in 2021, uses contrastive learning between images with textual representations, with some distance function like cosine similarity to align the embedding spaces.
➡️Flamingo uses a vision encoder pre-trained using CLIP and a Chinchilla pre-trained language model to represent the text. It introduces some special components — the Perceiver Resampler and a special Gated cross-attention — to combine those interleaved multi-modal representations, and is trained to predict next tokens. Flamingo can perform visual question answering or conversations around that content.
The above diagram represents that examples of visual dialogue from Flamingo paper
➡️BLIP-2 also uses pre-trained image and LLM encoders, connected by a Q-Former component. The model is trained for multiple tasks: matching images and text representations with both constrastive learning (like CLIP) and with binary classification task. It is also trained on images caption generation.
The illustration for this paper will be as 
Each of the below models represents a unique approach to integrating and aligning text and image data, showcasing the diverse methodologies within the field of VLMs. The choice of architecture and fusion strategy depends largely on the specific application and the nature of the tasks the model is designed to perform.
Architecture: Uses a transformer for text and a ResNet (or a Vision Transformer) for images. Fusion Strategy: Late fusion, with a focus on learning a joint embedding space. Alignment Method: Trained using contrastive learning, where image-text pairs are aligned in a shared embedding space.
Architecture: Based on the GPT-3 architecture, adapted to handle both text and image tokens. Fusion Strategy: Early to intermediate fusion, where text and image features are processed in an intertwined manner. Alignment Method: Uses an autoregressive model that understands text and image features in a sequential manner.
Architecture: A BERT-like model that processes both visual and textual information. Fusion Strategy: Intermediate fusion with cross-modal attention mechanisms. Alignment Method: Aligns text and image features using attention within a transformer framework.
Architecture:
Specifically designed for vision-and-language tasks, uses separate encoders for language and vision, followed by a cross-modality encoder. Fusion Strategy:
Intermediate fusion with a dedicated cross-modal encoder. Alignment Method:
Employs cross-modal attention between language and vision encoders.
Input Modalities:
VLMs: Handle both visual (images) and textual (language) inputs. LLMs: Primarily focused on processing and generating textual content.
Task Versatility:
VLMs: Capable of tasks that require understanding and correlating information from both visual and textual data, like image captioning, visual storytelling, etc. LLMs: Specialize in tasks that involve only text, such as language translation, text generation, question answering purely based on text, etc. Complexity in Integration: VLMs involve a more complex architecture due to the need to integrate and correlate information from two different modalities (visual and textual), whereas LLMs deal with a single modality.
Use Cases:
VLMs are particularly useful in scenarios where both visual and textual understanding is crucial, such as in social media analysis, where both image and text content are prevalent. LLMs are more focused on applications like text summarization, chatbots, and content creation where the primary medium is text. In summary, while both VLMs and LLMs are advanced AI models leveraging deep learning, VLMs stand out for their ability to understand and synthesize information from both visual and textual data, offering a broader range of applications that require multimodal understanding. Connecting Vision and Language Via VLMs Vision-Language Models (VLMs) are designed to understand and generate content that combines both visual and textual data. To effectively integrate these two distinct modalities—vision and language—VLMs use specialized mechanisms, such as adapters and linear layers. This section details popular building blocks that various VLMs utilize to link visual and language input. Let’s delve into how these components work in the context of VLMs. Adapters/MLPs/Fully Connected Layers in VLMs
Vision-Language Models (VLMs) integrate both visual (image) and textual (language) information processing. They are designed to understand and generate content that involves both images and text, enabling them to perform tasks like image captioning, visual question answering, and text-to-image generation. This primer offers an overview of their architecture and how they differ from Large Language Models (LLMs).
Adapters are small neural network modules inserted into pre-existing models. In the context of VLMs, they facilitate the integration of visual and textual data by transforming the representations from one modality to be compatible with the other.
Functioning:
Adapters typically consist of a few fully connected layers (put simply, a Multi-Layer Perceptron). They take the output from one type of encoder (say, a vision encoder) and transform it into a format that is suitable for processing by another type of encoder or decoder (like a language model).
Role of Linear Layers:
Linear layers, or fully connected layers, are a fundamental component in neural networks. In VLMs, they are crucial for processing the output of vision encoders.
Processing Vision Encoder Output:
After an image is processed through a vision encoder (like a CNN or a transformer-based vision model), the resulting feature representation needs to be adapted to be useful for language tasks. Linear layers can transform these vision features into a format that is compatible with the text modality.
Combining Modalities:
In a VLM, after processing through adapters and linear layers, the transformed visual data can be combined with textual data. This combination typically occurs before or within the language model, allowing the VLM to generate responses or analyses that incorporate both visual and textual understanding.
End-to-End Training:
In some advanced VLMs, the entire model, including vision encoders, linear layers, and language models, can be trained end-to-end. This approach allows the model to better learn how to integrate and interpret both visual and textual information.
Flexibility:
Adapters offer flexibility in model training. They allow for fine-tuning a pre-trained model on a specific task without the need to retrain the entire model. This is particularly useful in VLMs where training from scratch is often computationally expensive.
In summary, adapters and linear layers in VLMs serve as critical components for bridging the gap between visual and textual modalities, enabling these models to perform tasks that require an understanding of both images and text.