Multi-Modal Language Models
Introduction to Multi-Modal Language Models
Multi-Modal Language Models (MLMs) represent a paradigm shift in artificial intelligence, extending the capabilities of traditional language models to understand and generate content across multiple modalities including vision, audio, video, and text. These models bridge the gap between different sensory inputs, enabling more natural and comprehensive AI interactions.
Historical Evolution
Early Foundations (2010-2015)
Visual-Semantic Embeddings: Early work focused on learning joint representations between images and text. - DeViSE (2013): Deep Visual-Semantic Embeddings using ImageNet and Skip-gram - Word2VisualVec (2015): Learning visual features from textual descriptions
Mathematical Foundation: \(\(\mathbf{v}_{\text{image}} = f_{\text{CNN}}(\mathbf{I})\)\) \(\(\mathbf{v}_{\text{text}} = f_{\text{embedding}}(\mathbf{T})\)\) \(\(\text{similarity} = \cos(\mathbf{v}_{\text{image}}, \mathbf{v}_{\text{text}})\)\)
Vision-Language Revolution (2015-2020)
Attention-Based Models: Introduction of attention mechanisms for cross-modal understanding. - Show, Attend and Tell (2015): Visual attention for image captioning - VQA (2015): Visual Question Answering datasets and models - BERT (2018): Bidirectional encoder representations from transformers
Cross-Modal Attention: \(\(\alpha_{i,j} = \frac{\exp(e_{i,j})}{\sum_{k=1}^{K} \exp(e_{i,k})}\)\) \(\(e_{i,j} = \mathbf{W}^T \tanh(\mathbf{W}_v \mathbf{v}_j + \mathbf{W}_h \mathbf{h}_i)\)\) \(\(\mathbf{c}_i = \sum_{j=1}^{K} \alpha_{i,j} \mathbf{v}_j\)\)
Transformer Era (2020-Present)
Large-Scale Pre-training: Emergence of transformer-based multi-modal models. - CLIP (2021): Contrastive Language-Image Pre-training - DALL-E (2021): Text-to-image generation - GPT-4V (2023): Large-scale vision-language reasoning
Types of Multi-Modal Language Models
1. Vision-Language Models (VLMs)
Core Capability: Understanding and generating content that combines visual and textual information.
Key Models: - CLIP: Contrastive pre-training for zero-shot classification - BLIP: Bootstrapped vision-language pre-training - LLaVA: Large language and vision assistant - Flamingo: Few-shot learning with frozen LLMs
Applications: - Image captioning and visual question answering - Text-to-image generation (DALL-E, Midjourney, Stable Diffusion) - Visual reasoning and scene understanding - Document analysis and OCR
2. Audio-Language Models (ALMs)
Core Capability: Processing and generating audio content with textual understanding.
Key Models: - Whisper: Robust speech recognition across languages - SpeechT5: Unified pre-training for speech and text - AudioLM: Language modeling approach to audio generation - MusicLM: Generating music from text descriptions
Mathematical Framework: \(\(P(\mathbf{a}_{1:T}) = \prod_{t=1}^{T} P(\mathbf{a}_t | \mathbf{a}_{<t}, \mathbf{c})\)\)
Where \(\mathbf{a}_t\) represents audio tokens and \(\mathbf{c}\) is the conditioning text.
Applications: - Speech recognition and synthesis - Music generation and audio editing - Audio captioning and sound event detection - Voice assistants and conversational AI
3. Video-Language Models
Core Capability: Understanding temporal dynamics in video with textual descriptions.
Key Models: - VideoBERT: Joint modeling of video and language - Video-ChatGPT: Conversational video understanding - VideoLLaMA: Video-language instruction tuning - Sora: Text-to-video generation
Temporal Modeling: \(\(\mathbf{h}_t = \text{Transformer}(\mathbf{v}_t, \mathbf{h}_{t-1})\)\) \(\(\mathbf{v}_t = \text{FrameEncoder}(\mathbf{I}_t)\)\)
4. Multi-Modal Foundation Models
Core Capability: Unified understanding across multiple modalities simultaneously.
Key Models: - GPT-4V: Vision and language reasoning - Gemini: Multi-modal reasoning at scale - LLaVA-NeXT: Enhanced multi-modal capabilities - Qwen-VL: Large-scale vision-language model
Unified Architecture: \(\(\mathbf{h}_{\text{unified}} = \text{Transformer}([\mathbf{e}_{\text{text}}, \mathbf{e}_{\text{vision}}, \mathbf{e}_{\text{audio}}])\)\)
Training Paradigms
Contrastive Learning
Principle: Learn representations by contrasting positive and negative pairs.
Masked Language Modeling
Principle: Predict masked tokens across modalities.
Instruction Tuning
Principle: Fine-tune on instruction-following datasets.
Current Challenges and Future Directions
Technical Challenges
- Alignment: Ensuring consistent representations across modalities
- Scalability: Training on massive multi-modal datasets
- Efficiency: Reducing computational requirements
- Evaluation: Developing comprehensive benchmarks
Emerging Trends
- Unified Architectures: Single models handling all modalities
- Real-time Processing: Low-latency multi-modal understanding
- Embodied AI: Integration with robotics and physical systems
- Personalization: Adapting to individual user preferences
Key Resources
Datasets: - COCO: Common Objects in Context - Conceptual Captions: Large-scale image-text pairs - AudioSet: Large-scale audio event dataset - HowTo100M: Instructional video dataset
Evaluation Benchmarks: - VQA: Visual Question Answering - GLUE: General Language Understanding - MMBench: Multi-modal benchmark
Modern Vision-Language Models
Flamingo: Few-Shot Learning with Frozen LLMs
Paper: Flamingo: a Visual Language Model for Few-Shot Learning (NeurIPS 2022)
Code: Official Implementation | Open-source Implementation
Architecture Innovation: Integrate vision into frozen language models without catastrophic forgetting.
Key Components
1. Perceiver Resampler: - Input: Variable number of image features \(\mathbf{Z}_{\text{image}} \in \mathbb{R}^{N \times d}\) - Output: Fixed number of visual tokens \(\mathbf{V}_{\text{tokens}} \in \mathbb{R}^{M \times d}\) - Mechanism: Cross-attention between learned queries and image features
Mathematical Details: - Learned Queries: \(\mathbf{Q}_{\text{learned}} \in \mathbb{R}^{M \times d}\) are trainable parameters - Attention Mechanism: \(\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V\) - Multi-head Extension: \(\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h)W^O\)
2. Gated Cross-Attention: - Purpose: Inject visual information into language model layers - Gating: Allows model to ignore visual input when not needed
Gating Mechanism Details: - Initialization: \(\alpha\) is initialized to 0, ensuring no visual influence initially - Learning: \(\alpha = \tanh(\mathbf{W}_{\alpha} \mathbf{h}_{\text{LM}} + \mathbf{b}_{\alpha})\) (learnable gating) - Residual Connection: Preserves original LM capabilities while adding visual understanding
Training Strategy
Phase 1 - Vision Encoder Training: - Train CLIP-style contrastive learning - Freeze for subsequent phases
Phase 2 - Multimodal Training: - Freeze LLM weights - Train only Perceiver Resampler and Gated Cross-Attention - Use mixture of vision-language tasks
Few-Shot Prompting:
Image 1: [image] Caption: A cat sitting on a mat.
Image 2: [image] Caption: A dog running in a park.
Image 3: [image] Caption:
BLIP-2: Bootstrapping with Q-Former
Paper: BLIP-2: Bootstrapping Vision-Language Pre-training with Frozen Image Encoders and Large Language Models (ICML 2023)
Code: Official Implementation | Hugging Face
Innovation: Bridge frozen vision encoders and LLMs with a lightweight "Q-Former".
Q-Former Architecture
Design: Transformer with learnable query embeddings that interact with frozen image features.
Mathematical Foundation: - Query Embeddings: \(\mathbf{Q} \in \mathbb{R}^{N_q \times d}\) (typically \(N_q = 32\)) - Image Features: \(\mathbf{Z}_I \in \mathbb{R}^{N_p \times d}\) from frozen vision encoder - Text Embeddings: \(\mathbf{Z}_T \in \mathbb{R}^{N_t \times d}\) from text encoder
Two-Stage Training:
Stage 1 - Vision-Language Representation Learning:
Image-Text Contrastive (ITC): \(\(\mathcal{L}_{\text{ITC}} = -\frac{1}{B} \sum_{i=1}^{B} \log \frac{\exp(\text{sim}(q_i, t_i) / \tau)}{\sum_{j=1}^{B} \exp(\text{sim}(q_i, t_j) / \tau)}\)\) where \(q_i\) is the CLS token of Q-Former output, \(t_i\) is text representation, \(\tau\) is temperature.
Image-grounded Text Generation (ITG): \(\(\mathcal{L}_{\text{ITG}} = -\mathbb{E}_{(I,T)} \left[ \sum_{i=1}^{|T|} \log P(t_i | t_{<i}, \mathbf{Q}(I)) \right]\)\) where causal attention mask prevents queries from seeing future text tokens.
Image-Text Matching (ITM): \(\(\mathcal{L}_{\text{ITM}} = -\mathbb{E}_{(I,T,y)} [y \log P(y=1|I,T) + (1-y) \log P(y=0|I,T)]\)\) where \(y \in \{0,1\}\) indicates whether image-text pair is matched.
Multi-task Objective: \(\(\mathcal{L}_{\text{Stage1}} = \lambda_1 \mathcal{L}_{\text{ITC}} + \lambda_2 \mathcal{L}_{\text{ITG}} + \lambda_3 \mathcal{L}_{\text{ITM}}\)\)
Stage 2 - Vision-to-Language Generative Learning: - Connect Q-Former to frozen LLM via fully connected layer - Projection: \(\mathbf{H}_{\text{LLM}} = \text{Linear}(\mathbf{Q}_{\text{output}})\)
Where \(Q(I)\) represents the query embeddings from Q-Former conditioned on image \(I\).
Advantages
Efficiency: - Frozen components: No need to retrain large vision/language models - Lightweight bridge: Q-Former has only 188M parameters - Flexible: Can work with different vision encoders and LLMs
Performance: - State-of-the-art: Achieves best results on VQA, image captioning - Zero-shot: Strong performance without task-specific fine-tuning - Instruction following: Can follow complex multimodal instructions
LLaVA: Large Language and Vision Assistant
Paper: Visual Instruction Tuning (NeurIPS 2023)
Code: Official Implementation | Hugging Face
Philosophy: Extend instruction-tuned LLMs to multimodal scenarios.
Architecture
Simple Design: 1. Vision Encoder: Pre-trained CLIP ViT-L/14 (\(f_v: \mathbb{R}^{H \times W \times 3} \rightarrow \mathbb{R}^{N \times D_v}\)) 2. Projection Layer: Linear layer to map visual features to LLM embedding space 3. Language Model: Vicuna (instruction-tuned LLaMA)
Visual Token Integration: \(\(\mathbf{H}_{\text{visual}} = \text{Linear}(\mathbf{Z}_{\text{visual}}) = \mathbf{W} \mathbf{Z}_{\text{visual}} + \mathbf{b}\)\) \(\(\mathbf{H}_{\text{sequence}} = [\mathbf{H}_{\text{text}}, \mathbf{H}_{\text{visual}}, \mathbf{H}_{\text{instruction}}]\)\)
Mathematical Details: - Vision Features: \(\mathbf{Z}_{\text{visual}} \in \mathbb{R}^{N \times D_v}\) where \(N = 256\) (16×16 patches) - Projection: \(\mathbf{W} \in \mathbb{R}^{D_{\text{LLM}} \times D_v}\), \(\mathbf{b} \in \mathbb{R}^{D_{\text{LLM}}}\) - Sequence Length: Total tokens = \(|\text{text}| + N + |\text{instruction}|\)
Training Pipeline
Stage 1 - Feature Alignment: - Dataset: CC3M image-caption pairs - Objective: Align visual features with language model embedding space - Trainable: Only the projection layer
Stage 2 - End-to-End Fine-tuning: - Dataset: GPT-4 generated instruction-following data - Objective: Standard language modeling loss - Trainable: Projection layer + LLM (LoRA fine-tuning)
Instruction Data Generation: 1. Seed: Use COCO captions as starting point 2. Expand: GPT-4 generates diverse questions about images 3. Answer: GPT-4 provides detailed answers using captions 4. Filter: Remove low-quality or repetitive examples
GPT-4V: Multimodal Reasoning at Scale
Paper: GPT-4V(ision) System Card (OpenAI 2023)
API: OpenAI Vision API | Azure OpenAI
Capabilities (based on public demonstrations): - Complex reasoning: Multi-step visual reasoning with chain-of-thought - OCR and document understanding: Read and analyze text in images - Chart and graph interpretation: Extract insights from visualizations - Spatial reasoning: Understand 3D relationships and layouts - Creative tasks: Generate stories from images, design suggestions - Code generation: Convert UI mockups to functional code
Training Insights (speculated from papers and demonstrations): - Massive scale: Likely trained on billions of image-text pairs - Diverse data: Web images, documents, charts, diagrams, artwork, screenshots - Instruction tuning: Extensive human feedback on multimodal tasks - Safety alignment: Careful filtering and alignment for responsible AI - Constitutional AI: Self-supervised safety training
Architectural Speculation: - Vision Processing: Likely uses hierarchical vision transformers - Integration: Advanced cross-attention mechanisms between vision and language - Scaling: Estimated 1.7T+ parameters with mixture-of-experts - Training Objective: Multi-task learning with reinforcement learning from human feedback (RLHF)
LLaMA Vision: Open-Source Multimodal Foundation
Paper: LLaVA-1.5: Improved Baselines with Visual Instruction Tuning (2023)
Code: LLaVA Repository | LLaMA-Adapter-V2
Philosophy: Democratize multimodal AI with open-source vision-language capabilities.
Architecture
Core Components: 1. Vision Encoder: CLIP ViT-L/14 or custom vision transformer 2. Cross-Modal Adapter: Learnable query tokens for vision-language alignment 3. Language Model: LLaMA 2/3 base models (7B, 13B, 70B variants)
Token Integration Strategy: \(\(\mathbf{Q}_{\text{visual}} = \text{LearnableQueries}(N_{\text{tokens}}) \in \mathbb{R}^{N_{\text{tokens}} \times d}\)\) \(\(\mathbf{V}_{\text{aligned}} = \text{CrossAttention}(\mathbf{Q}_{\text{visual}}, \mathbf{Z}_{\text{image}}, \mathbf{Z}_{\text{image}})\)\) \(\(\mathbf{H}_{\text{multimodal}} = [\mathbf{H}_{\text{text}}, \mathbf{V}_{\text{aligned}}]\)\)
Mathematical Framework: - Cross-Attention: \(\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V\) - Multi-Head: \(\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h)W^O\) - Gating: \(\mathbf{V}_{\text{gated}} = \sigma(\mathbf{W}_g \mathbf{V}_{\text{aligned}}) \odot \mathbf{V}_{\text{aligned}}\)
Training Strategy
Multi-Stage Training: 1. Vision-Language Pre-training: Large-scale image-text alignment 2. Instruction Tuning: Task-specific fine-tuning with human preferences 3. RLHF: Reinforcement learning from human feedback for safety
Key Features: - Open weights: Full model weights available for research - Scalable architecture: Supports various model sizes - Commercial friendly: Permissive licensing for applications - Strong performance: Competitive with proprietary models
Gemma Vision: Google's Efficient Multimodal Model
Paper: PaliGemma: A versatile 3B VLM for transfer (2024)
Code: Official Implementation | Hugging Face
Design Philosophy: Lightweight yet powerful vision-language understanding.
Architecture Highlights
Efficient Design: - Base Model: Gemma 2B/7B language models - Vision Processing: SigLIP vision encoder with attention pooling - Memory Efficient: Gradient checkpointing and mixed precision training
Vision Integration: \(\(\mathbf{F}_{\text{pooled}} = \text{AttentionPool}(\mathbf{F}_{\text{patch}}) = \sum_{i=1}^{N} \alpha_i \mathbf{F}_{\text{patch}}^{(i)}\)\) \(\(\mathbf{E}_{\text{visual}} = \text{MLP}(\mathbf{F}_{\text{pooled}}) = \text{GELU}(\mathbf{W}_1 \mathbf{F}_{\text{pooled}} + \mathbf{b}_1)\mathbf{W}_2 + \mathbf{b}_2\)\)
Attention Pooling Details: - Attention Weights: \(\alpha_i = \frac{\exp(\mathbf{w}^T \mathbf{F}_{\text{patch}}^{(i)})}{\sum_{j=1}^{N} \exp(\mathbf{w}^T \mathbf{F}_{\text{patch}}^{(j)})}\) - Learnable Query: \(\mathbf{w} \in \mathbb{R}^{d}\) is a learnable attention query vector - Output Dimension: \(\mathbf{E}_{\text{visual}} \in \mathbb{R}^{d_{\text{model}}}\) matches Gemma embedding dimension
Training Innovations
Curriculum Learning: 1. Simple Tasks: Basic image captioning and VQA 2. Complex Reasoning: Multi-step visual reasoning tasks 3. Domain Adaptation: Specialized datasets for specific applications
Efficiency Optimizations: - Knowledge Distillation: Learn from larger teacher models - Progressive Training: Gradually increase input resolution - Sparse Attention: Reduce computational overhead
Qwen2.5-VL: Advanced Chinese-English Multimodal Model
Paper: Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution (2024)
Code: Official Implementation | Hugging Face
Innovation: State-of-the-art multilingual vision-language understanding.
Technical Advances
Architecture Improvements: - Dynamic Resolution: Adaptive image resolution based on content complexity - Hierarchical Vision Encoding: Multi-scale feature extraction with pyramid structure - Cross-Lingual Alignment: Unified representation for multiple languages - Rotary Position Embedding: 2D positional encoding for vision tokens
Mathematical Framework: \(\(\mathbf{R}_{\text{adaptive}} = \text{ResolutionSelector}(\mathbf{I}, \text{complexity}) = \arg\max_{r \in \mathcal{R}} \text{Score}(\mathbf{I}, r)\)\) \(\(\mathbf{F}_{\text{multi-scale}} = \text{Pyramid}(\mathbf{I}_{\mathbf{R}_{\text{adaptive}}}) = \{\mathbf{F}_1, \mathbf{F}_2, ..., \mathbf{F}_L\}\)\)
Dynamic Resolution Details: - Complexity Score: \(\text{Score}(\mathbf{I}, r) = \lambda_1 \cdot \text{EdgeDensity}(\mathbf{I}_r) + \lambda_2 \cdot \text{TextDensity}(\mathbf{I}_r)\) - Resolution Set: \(\mathcal{R} = \{224, 448, 672, 896\}\) pixels - Pyramid Levels: \(L = 3\) with scales \(\{1, 0.5, 0.25\}\)
2D Rotary Position Embedding: \(\(\text{RoPE2D}(\mathbf{x}, m, n) = \mathbf{R}_m^{(x)} \mathbf{R}_n^{(y)} \mathbf{x}\)\) where \(\mathbf{R}_m^{(x)}\) and \(\mathbf{R}_n^{(y)}\) are rotation matrices for x and y coordinates.
Capabilities
Advanced Features: - Document Understanding: OCR, table parsing, layout analysis - Video Processing: Temporal reasoning across video frames - Code Generation: Visual programming and UI understanding - Mathematical Reasoning: Solve problems from visual inputs
Multilingual Support: - Chinese-English: Native bilingual understanding - Cross-lingual Transfer: Knowledge sharing between languages - Cultural Context: Understanding of cultural visual elements
GLM4.5-V: Conversational Vision Intelligence
Paper: GLM-4V: Open Multimodal Large Language Model (2024)
Code: Official Implementation | Hugging Face
Focus: Natural conversational interaction with visual content.
Architecture Design
Conversational Framework: - Context Awareness: Maintain visual context across dialogue turns - Memory Integration: Remember previous visual interactions - Reasoning Chain: Explicit step-by-step visual reasoning - Multi-turn Dialogue: Coherent conversation with visual references
Technical Components: \(\(\mathbf{C}_{t} = \text{ContextUpdate}(\mathbf{C}_{t-1}, \mathbf{V}_{t}, \mathbf{T}_{t}) = \text{LSTM}([\mathbf{C}_{t-1}; \mathbf{V}_{t}; \mathbf{T}_{t}])\)\) \(\(\mathbf{R}_{t} = \text{ReasoningChain}(\mathbf{C}_{t}, \text{Query}_{t}) = \text{Transformer}(\mathbf{C}_{t} \oplus \text{Query}_{t})\)\)
Mathematical Framework: - Context Vector: \(\mathbf{C}_{t} \in \mathbb{R}^{d_{\text{context}}}\) encodes dialogue history - Visual Memory: \(\mathbf{V}_{t} = \text{VisionEncoder}(\mathbf{I}_{t}) \in \mathbb{R}^{N_v \times d_v}\) - Text Memory: \(\mathbf{T}_{t} = \text{TextEncoder}(\text{utterance}_{t}) \in \mathbb{R}^{N_t \times d_t}\) - Reasoning Output: \(\mathbf{R}_{t} \in \mathbb{R}^{N_r \times d_r}\) contains step-by-step reasoning
Training Methodology
Dialogue-Centric Training: 1. Single-turn VQA: Basic visual question answering 2. Multi-turn Dialogue: Conversational visual understanding 3. Reasoning Tasks: Complex multi-step visual reasoning
Key Innovations: - Dialogue State Tracking: Maintain conversation context - Visual Memory: Remember and reference previous images - Explanation Generation: Provide reasoning for answers - Interactive Learning: Learn from user feedback
Comparative Analysis of Modern VLMs
| Model | Strengths | Use Cases | Training Scale | Key Innovation |
|---|---|---|---|---|
| Flamingo | Few-shot learning, frozen LLM | Research, adaptation | 1.8B image-text pairs | Perceiver Resampler + Gated Cross-Attention |
| BLIP-2 | Efficient bridging | General VL tasks | 129M image-text pairs | Q-Former architecture |
| LLaVA | Simple, effective | General VQA, research | 600K instruction data | Linear projection simplicity |
| GPT-4V | Advanced reasoning | Complex analysis | Billions of pairs | Massive scale + RLHF |
| LLaMA Vision | Open-source, scalable | Research, applications | Large-scale pre-training | Cross-modal adapter |
| Gemma Vision | Efficient, lightweight | Edge deployment | Optimized datasets | Attention pooling + SigLIP |
| Qwen2.5-VL | Multilingual, advanced | Document AI, video | Massive multilingual | Dynamic resolution + 2D RoPE |
| GLM4.5-V | Conversational | Interactive applications | Dialogue-focused | Context-aware reasoning |
Performance Benchmarks
Vision-Language Understanding: - VQAv2: GPT-4V (87.2%) > Qwen2.5-VL (84.3%) > LLaVA-1.5 (78.5%) - TextVQA: Qwen2.5-VL (78.6%) > GPT-4V (78.0%) > BLIP-2 (42.5%) - MMMU: GPT-4V (56.8%) > Gemma Vision (42.3%) > LLaVA-1.5 (35.7%)
Efficiency Metrics: - Parameters: Gemma Vision (3B) < LLaVA (7B) < Qwen2.5-VL (7B) < GLM4.5-V (9B) - Inference Speed: Gemma Vision > LLaVA > Qwen2.5-VL > GPT-4V - Memory Usage: Gemma Vision (6GB) < LLaVA (13GB) < Qwen2.5-VL (14GB)
Emerging Trends
Technical Evolution: 1. Efficiency: Smaller models with comparable performance 2. Multimodality: Beyond vision to audio, video, 3D 3. Reasoning: Enhanced logical and mathematical capabilities 4. Interaction: More natural conversational interfaces 5. Specialization: Domain-specific optimizations
Research Directions: - Few-shot Learning: Better generalization with limited data - Compositional Understanding: Complex scene decomposition - Temporal Reasoning: Video and sequential understanding - Embodied AI: Integration with robotics and physical systems - Multimodal Reasoning: Enhanced logical and mathematical capabilities - Efficient Architectures: Smaller models with comparable performance
Key Resources and Datasets
Training Datasets: - LAION-5B: Large-scale image-text dataset (5.85B pairs) - CC3M/CC12M: Conceptual Captions (3M/12M pairs) - COCO Captions: Microsoft COCO (330K images, 1.5M captions) - Visual Genome: Scene graphs and dense captions (108K images) - LLaVA-Instruct: GPT-4 generated instruction data (158K conversations)
Evaluation Benchmarks: - VQAv2: Visual Question Answering - General VQA - TextVQA: Text-based VQA - OCR and reading comprehension - MMMU: Massive Multi-discipline Multimodal Understanding - Expert-level reasoning - MMBench: Comprehensive VLM evaluation - SEED-Bench: Multimodal comprehension benchmark
Implementation Frameworks: - Transformers: Hugging Face library for VLM inference - LLaVA: Training and inference framework - BLIP: Salesforce BLIP family - OpenFlamingo: Open-source Flamingo implementation - MiniGPT-4: Lightweight VLM
Mathematical Foundations:
Cross-Modal Attention: \(\(\text{CrossAttn}(\mathbf{Q}_v, \mathbf{K}_t, \mathbf{V}_t) = \text{softmax}\left(\frac{\mathbf{Q}_v \mathbf{K}_t^T}{\sqrt{d_k}}\right) \mathbf{V}_t\)\)
Contrastive Learning Objective: \(\(\mathcal{L}_{\text{contrastive}} = -\frac{1}{N} \sum_{i=1}^{N} \log \frac{\exp(\text{sim}(v_i, t_i) / \tau)}{\sum_{j=1}^{N} \exp(\text{sim}(v_i, t_j) / \tau)}\)\)
Vision-Language Alignment: \(\(\mathcal{L}_{\text{alignment}} = \|\mathbf{f}_v(\mathbf{I}) - \mathbf{f}_t(\mathbf{T})\|_2^2\)\)
where \(\mathbf{f}_v\) and \(\mathbf{f}_t\) are vision and text encoders respectively.