"PixelWorld: Towards Perceiving Everything as Pixels"
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https://arxiv.org/abs/2501.19339
The current LLMs process text and visual inputs differently, unlike human perception. This paper addresses the need for a unified approach, especially for AI agents interacting with pixel-based environments.
This paper proposes "Perceive Everything as Pixels" (PEAP). It evaluates models using PixelWorld, a new suite where all inputs are pixels, to test model performance across modalities.
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📌 PixelWorld offers a unified input space, processing all modalities as pixels. This bypasses modality-specific preprocessing. It allows Vision Language Models to handle diverse data natively, enhancing real-world applicability.
📌 PEAP-Fast leverages spatial sparsity by pruning blank pixel patches. This optimization significantly reduces computational overhead by 82.98% with minimal accuracy impact, making pixel-based input more practical.
📌 Attention analysis reveals vision encoders can serve as universal tokenizers. Similar attention patterns between pixel and token inputs suggest a potential shift towards vision-centric foundation models for multimodal tasks.
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Methods Explored in this Paper 🔧:
→ The paper introduces PixelWorld, a novel evaluation suite. It converts various data types, including text, tables, and multimodal content, into pixel format.
→ This pixel conversion is achieved through image synthesis for text and structured data. For multimodal data, OCR is used, or original textual components are utilized.
→ The paper evaluates models using this pixel-based input method, named PEAP, and compares it against traditional token-based text input.
→ To improve efficiency, they propose PEAP-Fast. PEAP-Fast removes blank pixel patches, reducing computational load during inference without losing accuracy. Positional embeddings are preserved for layout retention.
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Key Insights 💡:
→ PEAP enhances performance in multimodal tasks like website and document understanding by mitigating OCR errors and preserving layout.
→ Reasoning and coding tasks see performance declines with pixel input across models, highlighting a gap in perceptual abilities for complex tasks.
→ Larger models show better consistency between pixel and token input performance. Smaller models struggle more with pixel input and instruction following.
→ Attention patterns in models using PEAP are similar to those with token-based input, suggesting vision encoders can act as universal tokenizers.
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Results 📊:
→ PEAP-Fast reduces inference overhead by 82.98% compared to PEAP, with only a minor 1.17% accuracy drop on SuperGLUE.
→ On SlidesVQA, Gemini-Flash improves by 34.24 points and Qwen2VL-7B by 23.55 points using pixel input compared to text-only baselines.
→ GPT-4o's performance on ARC dataset drops by only 0.59 points from text to pixel input, while Qwen2-VL-2B drops by 21.73 points.