LMM-ConceptExplainer-VQA

This project demonstrates a concept-based explainability framework for Large Multimodal Models (LMMs) using the VQAv2-small dataset. It adapts techniques inspired by the paper "A Concept-Based Explainability Framework for Large Multimodal Models" to provide interpretable visual question answering (VQA) outputs by analyzing how specific words in answers are represented across text and vision modalities.

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📌 Objective

To identify and explain key entities (concepts) from VQA answers using both textual and visual embeddings extracted via a Large Multimodal Model (LMM), and to assess their stability and representational utility.

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📂 Dataset

• Name: VQAv2-small
• Accessed via HuggingFace's datasets library.
• A lightweight version of the VQAv2 dataset used for development and experimentation.

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🧪 Methodology

1. Extract answers from a subset of the VQAv2 dataset.
2. Identify and select 5 frequently occurring target entities (e.g., “dog”, “clock”, “woman”).
3. Use a pretrained LMM (e.g., CLIP or BLIP) to extract:
   • Textual embeddings of the selected words.
   • Visual embeddings from images whose answers contain those words.
4. Visualize embeddings and compare alignment between text and image representations.
5. Evaluate each entity’s:
   • Utility: How meaningfully the entity is represented.
   • Stability: How consistently the entity is represented across samples.

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🧠 Concept-Based Explainability

This project implements a concept-based explanation approach, inspired by research that seeks to understand how multimodal models internally represent human-interpretable concepts.

🧩 Key Concepts

1. Entity Selection

• Identify 5 target words/entities from the answers dataset.
• These words become the concepts whose representations are analyzed.

2. Representation Extraction

• For each entity:
  • Textual Representation: From the model's text encoder (e.g., the word "dog").
  • Visual Representation: From visual encoder using images where the answer includes the word.

3. Concept Verification

• Use cosine similarity and dimensionality reduction to verify that:
  • Images associated with the same word produce similar embeddings.
  • These are close to the word’s text embedding.

4. Utility Evaluation

• Check if representations are:
  • Distinct: Do different concepts have clearly separated embeddings?
  • Cohesive: Are instances of the same concept tightly clustered?

5. Stability Check

• Analyze the variance in representations of the same entity across multiple samples.
• Determine how sensitive concept embeddings are to input variations.

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📈 Results

• The notebook demonstrates:
  • Visual and textual embedding extraction.
  • Similarity analysis between representations.
  • Dimensionality reduction plots.
  • Entity-wise clustering consistency and variance metrics.

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📦 Requirements

Install the dependencies using:

Required packages include:

• transformers

• datasets

• torch

• matplotlib

• sklearn

• PIL

• numpy

📄 License

MIT License

🙋‍♀️ Acknowledgments

• Based on methods adapted from the paper: "A Concept-Based Explainability Framework for Large Multimodal Models"

• Uses the VQAv2 dataset from HuggingFace Datasets.

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