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ViT-CoMer: Enhancing Vision Transformer for Dense Predictions


Core Concepts
ViT-CoMer enhances the ViT backbone by integrating multi-scale convolutional features, improving performance in dense prediction tasks.
Abstract
ViT-CoMer introduces a plain, pre-training-free, and feature-enhanced ViT backbone with Convolutional Multi-scale feature interaction. It addresses limitations of ViT in dense prediction tasks by incorporating spatial pyramid convolutional features and bidirectional fusion interaction between CNN and transformer. The model achieves competitive results on COCO val2017 and ADE20K val datasets without extra training data. The content discusses the challenges faced by Vision Transformers (ViTs) in dense prediction tasks due to limited local information interaction and single-feature representation. It proposes ViT-CoMer as a solution that integrates multi-receptive field feature pyramid module (MRFP) and CNN-Transformer bidirectional fusion interaction module (CTI). By leveraging open-source pre-trained weights, ViT-CoMer demonstrates improved performance across various dense prediction benchmarks. The study compares ViT-CoMer with existing methods such as Swin, PVT, MixFormer, and others on object detection, instance segmentation, and semantic segmentation tasks. Results show that ViT-CoMer outperforms other backbones under similar model sizes and configurations. Ablation studies confirm the effectiveness of MRFP and CTI modules in enhancing plain ViTs for dense predictions. Further experiments demonstrate the scalability of ViT-CoMer when applied to hierarchical vision transformers like Swin-T. Qualitative results showcase the model's ability to capture fine-grained multi-scale features for improved object localization. Overall, ViT-CoMer proves to be a promising backbone for dense prediction tasks in computer vision.
Stats
Our ViT-CoMer-L achieves 64.3% AP on COCO val2017 without extra training data. Our ViT-CoMer-L attains 62.1% mIoU on ADE20K val. The proposed MRFP module provides rich multi-scale information. CTI fuses multi-scale features from CNN and Transformer. N=4 bidirectional fusion interaction modules perform best.
Quotes
"Our main contributions are proposing a novel dense prediction backbone by combining plain ViT with CNN features." "We evaluate our proposed ViT-CoMer on several challenging dense prediction benchmarks." "Notably, our approach can achieve superior performance compared to both plain and adapted backbones."

Key Insights Distilled From

by Chunlong Xia... at arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07392.pdf
ViT-CoMer

Deeper Inquiries

How does the integration of CNN features enhance the performance of Vision Transformers

The integration of CNN features enhances the performance of Vision Transformers by addressing key limitations in ViT models. CNNs excel at capturing local information and multi-scale features, which are crucial for dense prediction tasks like object detection and segmentation. By combining CNN features with ViT, the model can benefit from the strengths of both architectures. The CNN features provide richer spatial information and diverse feature scales that complement the global context captured by transformers. This fusion allows for better interaction between local and global features, leading to improved performance in tasks requiring detailed spatial understanding.

What are the potential limitations or drawbacks of using advanced pre-trained weights in models like ViT-CoMer

While using advanced pre-trained weights like those in models such as ViT-CoMer can offer significant benefits in terms of initialization and generalization, there are potential limitations to consider. One drawback is the risk of overfitting to specific datasets or domains present in the pre-training data. Models may struggle to adapt effectively to new or different datasets if they rely heavily on pre-trained weights that do not generalize well across various scenarios. Additionally, advanced pre-training often requires substantial computational resources and time-consuming training processes, making it less accessible for researchers without access to high-performance computing infrastructure.

How might the concept of bidirectional fusion interaction impact future developments in computer vision research

The concept of bidirectional fusion interaction has the potential to shape future developments in computer vision research significantly. By enabling effective communication between different architectural components like CNNs and transformers bidirectionally, models can leverage complementary strengths more efficiently. This approach opens up possibilities for creating hybrid architectures that combine specialized capabilities from different paradigms seamlessly. Bidirectional fusion interaction could lead to more robust models capable of handling a wide range of tasks with improved accuracy and efficiency by leveraging diverse sources of information effectively.
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