Visual Transformers: Token-based Image Representation and Processing for Computer Vision (2006.03677v4)

Abstract: Computer vision has achieved remarkable success by (a) representing images as uniformly-arranged pixel arrays and (b) convolving highly-localized features. However, convolutions treat all image pixels equally regardless of importance; explicitly model all concepts across all images, regardless of content; and struggle to relate spatially-distant concepts. In this work, we challenge this paradigm by (a) representing images as semantic visual tokens and (b) running transformers to densely model token relationships. Critically, our Visual Transformer operates in a semantic token space, judiciously attending to different image parts based on context. This is in sharp contrast to pixel-space transformers that require orders-of-magnitude more compute. Using an advanced training recipe, our VTs significantly outperform their convolutional counterparts, raising ResNet accuracy on ImageNet top-1 by 4.6 to 7 points while using fewer FLOPs and parameters. For semantic segmentation on LIP and COCO-stuff, VT-based feature pyramid networks (FPN) achieve 0.35 points higher mIoU while reducing the FPN module's FLOPs by 6.5x.

• The paper introduces semantic visual tokens that abstract key image regions, shifting the paradigm from pixel-based processing to contextual token representation.
• The paper employs Visual Transformers that leverage self-attention to model dense token relationships, achieving up to 7-point accuracy gains and a 6.9x reduction in FLOPs.
• The paper extends its approach to semantic segmentation, demonstrating improved mIoU on benchmarks like LIP and COCO-stuff while significantly reducing computational costs.

Visual Transformers: Token-based Image Representation and Processing for Computer Vision

The paper "Visual Transformers: Token-based Image Representation and Processing for Computer Vision" introduces a novel approach to image processing and representation in computer vision by transitioning from the traditional pixel-convolution paradigm to a token-transformer paradigm. This transition addresses the inherent inefficiencies found when utilizing convolutions, which uniformly process all image pixels without considering contextual importance.

Key Contributions

1. Semantic Visual Tokens: The paper proposes representing images using semantic visual tokens instead of pixel arrays. This abstraction allows the model to focus on significant image regions and high-level concepts while ignoring less pertinent information.
2. Visual Transformers (VTs): Visual Transformers replace convolutions with transformers, enabling dense modeling of token relationships. By operating in token space rather than pixel space, VTs leverage self-attention mechanisms to understand spatially distant relationships effectively.
3. Token Efficiency: VTs achieve state-of-the-art performance using significantly fewer floating-point operations (FLOPs) and parameters compared to convolutional counterparts. Notably, the paper highlights a reduction in FLOPs by up to 6.9x during the last stage of a ResNet model while increasing ImageNet accuracy by 4.6 to 7 percentage points.
4. Application to Semantic Segmentation: The VT model is extended to semantic segmentation tasks. Employing transformer-based feature pyramid networks (FPN), VTs achieve superior performance metrics, such as 0.35 points higher mean intersection-over-union (mIoU) on datasets like LIP and COCO-stuff with a 6.5x reduction in FLOPs.

Experimental Analysis

The authors conducted comprehensive experiments to validate the efficacy of VTs, including:

• Image Classification on ImageNet: VTs showcased significant improvement of up to 7 percentage points in top-1 accuracy, utilizing fewer resources.
• Semantic Segmentation: The VT-FPN model demonstrated enhanced segmentation accuracy with reduced computational cost, highlighting its potential for practical applications in processing high-resolution images.

Relationships with Current Trends

The paper contextualizes its contributions within the broader scope of introducing transformers to vision tasks. It contrasts with prior works like Vision Transformer (ViT) and DETR, while outlining differences from graph convolutions and various attention mechanisms that have been integrated into vision models.

Implications and Future Prospects

By focusing on the efficiency of semantic representation and processing, VTs have the potential to redefine the landscape of computer vision, especially concerning resource-constrained environments. The ability to process high-level concepts with less computational overhead paves the way for creating more optimized and scalable vision models.

Future developments could explore:

• Integration of VTs with neural architecture search to further enhance model architecture design.
• Synergizing VTs with larger and more diverse datasets to push the boundaries of vision models even further.
• Expanding VT applications beyond typical benchmarks to real-world scenarios requiring efficient semantic understanding.

Conclusion

The paper presents a compelling case for the utilization of Visual Transformers in supplanting traditional convolution-based methods. By focusing on semantic tokens and leveraging transformers for modeling inter-token relationships, the research provides a substantial theoretical and practical foundation to streamline image representation and processing. As AI progresses, innovations like VTs will be critical in addressing the rapidly increasing data complexities inherent in vision-centric applications.