Bridging the gap between visual perception and language reasoning by integrating a Vision Transformer (ViT) encoder with a decoder-only large language model through a lightweight projection module. The system is instruction-tuned on diverse image-text pairs to enable zero-shot visual question answering, image captioning, spatial grounding, and multi-turn visual dialogue -- delivering human-like scene understanding without task-specific fine-tuning.
Large language models have demonstrated remarkable reasoning capabilities across text-based tasks, yet they remain fundamentally blind -- unable to perceive, interpret, or reason about visual information. Conversely, vision models excel at extracting rich spatial and semantic features from images but lack the capacity for open-ended language generation and multi-step reasoning.
Current approaches to multimodal understanding often produce responses that are superficial, ungrounded, or hallucinatory. Models struggle to provide spatially accurate descriptions, count objects reliably, understand spatial relationships, or answer complex compositional questions that require both visual perception and logical inference. The core challenge lies in building a bridge between the visual feature space and the language embedding space that preserves fine-grained visual detail while enabling the LLM to reason over it naturally.
This project addresses that gap by designing an instruction-tuned multimodal architecture that fuses a frozen Vision Transformer with a decoder-only LLM through a learnable projection module, enabling the model to follow diverse visual instructions and generate grounded, contextually accurate responses across a wide range of tasks.
From raw image and text inputs through visual encoding, cross-modal alignment, instruction tuning, to multi-task output. Hover each stage for details on the processing at that step.
Input images resized and normalized. Supports natural images, charts, documents, and screenshots for diverse visual understanding tasks.
Pre-trained ViT-L/14 splits image into 16×16 patches and encodes them through 24 transformer layers, producing 256 visual tokens.
Two-layer MLP projects visual tokens from ViT embedding space into the LLM’s token embedding space, bridging the vision-language gap.
User instruction or question is tokenized and prepended/appended to the visual token sequence, forming the multimodal input context.
7B parameter transformer processes interleaved visual and text tokens via causal attention. Generates text autoregressively conditioned on the full multimodal context.
150K curated image-instruction-response triplets covering VQA, captioning, referring expressions, and visual reasoning tasks.
LoRA adapters added to LLM attention layers. Only adapter weights are trained, freezing the visual encoder and base LLM weights for efficiency.
The training procedure follows a carefully staged pipeline designed to progressively align visual representations with the language model's embedding space, culminating in multi-task instruction tuning that enables the model to handle diverse visual-language tasks from a single unified architecture.
Employ a pretrained ViT backbone to encode input images into a sequence of patch-level feature tokens, capturing fine-grained spatial and semantic information at high resolution.
A lightweight MLP-based projection module maps the ViT output embeddings into the LLM's word embedding space, aligning visual tokens with language tokens for seamless cross-modal fusion.
The model is fine-tuned on diverse image-text instruction pairs spanning visual question answering, detailed captioning, and multi-turn conversations to learn instruction-following behavior.
Joint training across VQA, captioning, visual grounding, and referring expression comprehension enables the model to generalize across tasks without task-specific heads or adapters.
Comparison across question categories
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Multi-axis evaluation against baselines
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Cross-entropy loss over training epochs
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Scores across standard evaluation suites
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A simple two-layer MLP with GELU activation is sufficient to align visual features with language embeddings. This keeps the trainable parameter count low while achieving strong cross-modal transfer, avoiding the need for heavyweight cross-attention modules.
Instruction tuning on diverse visual tasks enables strong zero-shot generalization to unseen question types, image domains, and compositional queries without any task-specific fine-tuning or prompt engineering at inference time.
By training on referring expression and visual grounding data, the model learns to spatially localize objects in images and associate them with language descriptions, producing responses that are visually faithful rather than hallucinated.
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