"EVEv2: Improved Baselines for Encoder-Free Vision-Language Models"

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https://arxiv.org/abs/2502.06788

The challenge is building efficient Vision-Language Models without pre-trained vision encoders due to limitations and complexities. This paper addresses the difficulty of learning visual perception from scratch within a unified model and minimizing vision-language interference.

This paper proposes EVEv2.0. EVEv2.0 is an improved encoder-free VLM family. EVEv2.0 uses a divide-and-conquer architecture and optimized training.

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📌 EVEv2.0's modality-specific transformer blocks, with decoupled attention, Layer Normalization, and Feed-Forward Networks, directly address cross-modal interference. This architectural choice enables effective vision learning within a unified model.

📌 EVEv2.0's staged training efficiently learns visual perception from scratch. Freezing the LLM initially and then progressively training vision layers and finally the full model optimizes knowledge retention and multimodal alignment.

📌 EVEv2.0's patch embedding layer and divide-and-conquer architecture offer a pathway to efficient encoder-free Vision-Language Models. Lossless visual encoding and reduced inductive bias can improve flexibility and data scaling.

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Methods Explored in this Paper 🔧:

→ Introduces EVEv2.0, an encoder-free VLM architecture. EVEv2.0 uses a divide-and-conquer strategy.

→ This architecture fully disentangles model components. It incorporates modality-wise sparsity within a decoder-only backbone.

→ EVEv2.0 employs a minimalist patch embedding layer for visual input, learning vision from scratch. This eliminates biases from pre-trained vision encoders.

→ The model utilizes a decomposed transformer block. This block features modality-specific components for attention, Layer Normalization, and Feed-Forward Network.

→ This design minimizes interference between vision and language modalities within the unified model. It supports efficient visual perception learning and knowledge retention from pre-trained LLMs.

→ EVEv2.0 is trained in four stages. These stages are LLM-guided pre-aligning, vision perception learning, vision-text fully-aligning, and supervised fine-tuning.

→ The training process uses a large dataset. This dataset is created using an enhanced captioning engine called DenseFusion++.

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Key Insights 💡:

→ Decomposing the VLM architecture with modality-aware components is crucial. It effectively reduces vision-language interference.

→ A well-designed training strategy is essential for encoder-free VLMs. This includes using high-quality image-text data and a staged training approach.

→ Encoder-free VLMs can achieve strong vision perception and reasoning capabilities. They can approach the performance of encoder-based models with efficient data scaling.

→ EVEv2.0 demonstrates superior data efficiency compared to previous encoder-free VLM approaches. This is due to its architecture and training methodology.

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Results 📊:

→ EVEv2.0 outperforms prior encoder-free VLMs across various benchmarks.

→ EVEv2.0 achieves comparable performance to encoder-based VLMs of similar capacity.

→ Using 100M public data, EVEv2.0 demonstrates strong vision-reasoning capability and data efficiency.