Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding (2501.07783v1)

Abstract: Image pyramids are widely adopted in top-performing methods to obtain multi-scale features for precise visual perception and understanding. However, current image pyramids use the same large-scale model to process multiple resolutions of images, leading to significant computational cost. To address this challenge, we propose a novel network architecture, called Parameter-Inverted Image Pyramid Networks (PIIP). Specifically, PIIP uses pretrained models (ViTs or CNNs) as branches to process multi-scale images, where images of higher resolutions are processed by smaller network branches to balance computational cost and performance. To integrate information from different spatial scales, we further propose a novel cross-branch feature interaction mechanism. To validate PIIP, we apply it to various perception models and a representative multimodal LLM called LLaVA, and conduct extensive experiments on various tasks such as object detection, segmentation, image classification and multimodal understanding. PIIP achieves superior performance compared to single-branch and existing multi-resolution approaches with lower computational cost. When applied to InternViT-6B, a large-scale vision foundation model, PIIP can improve its performance by 1%-2% on detection and segmentation with only 40%-60% of the original computation, finally achieving 60.0 box AP on MS COCO and 59.7 mIoU on ADE20K. For multimodal understanding, our PIIP-LLaVA achieves 73.0% accuracy on TextVQA and 74.5% on MMBench with only 2.8M training data. Our code is released at https://github.com/OpenGVLab/PIIP.

• The paper introduces a novel parameter-inverted design that uses smaller networks for high-resolution images, reducing computational cost by 40-60% while enhancing performance by 1-2%.
• It leverages pretrained models like Vision Transformers and CNNs to enable cross-branch feature fusion across multiple scales for tasks including detection, segmentation, and classification.
• Validation on benchmarks such as MS COCO, ADE20K, TextVQA, and MMBench demonstrates improved efficiency and accuracy for visual perception and multimodal understanding.

Overview of Parameter-Inverted Image Pyramid Networks

The paper "Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding" introduces a novel network architecture—Parameter-Inverted Image Pyramid Networks (PIIP)—designed to address the substantial computational overhead associated with traditional image pyramids in visual perception tasks. Standard image pyramid approaches apply the same large-scale model across various image resolutions, often resulting in increased computational costs. The proposed PIIP framework mitigates this by employing a parameter-inverted design wherein more diminutive networks process higher-resolution images.

PIIP leverages pretrained models like Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) to handle multi-scale images. The images processed by smaller network branches facilitate both computational efficiency and high performance. Significantly, PIIP introduces a cross-branch feature interaction mechanism that enables feature fusion across different resolutions.

The architecture is validated across multiple tasks, including object detection, segmentation, image classification, and multimodal understanding. PIIP demonstrates superior performance compared to traditional single-branch and other multi-resolution methods while reducing computational costs.

Key Contrasts and Numerical Outcomes

PIIP presents a notable improvement in computational efficiency. For instance, when applied to the InternViT-6B model, PIIP enhances detection and segmentation performance by 1 to 2% while only using 40 to 60% of the original computational requirement. The framework achieves a 60.0 box AP on MS COCO and 59.7 mIoU on ADE20K. Additionally, in the domain of multimodal understanding, PIIP-LLaVA delivers 73.0% accuracy on TextVQA and 74.5% on MMBench even with a limited dataset of 2.8 million training examples.

The PIIP framework also underscores the potential for integrating pretrained vision and LLMs to achieve context-aware multimodal understanding, further moderated by its computational efficiency.

Theoretical and Practical Implications

The introduction of PIIP manifests both theoretical and practical implications. Theoretically, it challenges conventional paradigms within visual perception and multimodal frameworks by rethinking the interplay between image resolution and model size—effectively showcasing a parameter-inverted approach. Practically, PIIP significantly alleviates computational burdens, making high-resolution visual perception tasks more feasible across varying resource constraints.

This parameter-inverted approach potentially envisions new possibilities for efficiently leveraging large-scale pretrained models across diverse applications in AI, including but not limited to autonomous systems, robotics, and interactive AI assistants.

Future Directions

Although PIIP marks a substantial improvement in efficiency and performance, future research could explore further optimizing interaction mechanisms between branches. Additional efforts might aim to refine the approach's adaptability across other multimodal models and contexts. Another avenue for exploration is fine-tuning the balance between resolution and model parameterization to further minimize computational costs while maximizing performance gains.