Learning to Upsample by Learning to Sample (2308.15085v1)

Abstract: We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much workload, mostly due to the time-consuming dynamic convolution and the additional sub-network used to generate dynamic kernels. Further, the need for high-res feature guidance of FADE and SAPA somehow limits their application scenarios. To address these concerns, we bypass dynamic convolution and formulate upsampling from the perspective of point sampling, which is more resource-efficient and can be easily implemented with the standard built-in function in PyTorch. We first showcase a naive design, and then demonstrate how to strengthen its upsampling behavior step by step towards our new upsampler, DySample. Compared with former kernel-based dynamic upsamplers, DySample requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency. Besides the light-weight characteristics, DySample outperforms other upsamplers across five dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation. Code is available at https://github.com/tiny-smart/dysample.

• The paper introduces a novel point sampling approach for upsampling that reduces computational overhead compared to traditional kernel-based methods.
• It employs dynamic scope offset generation and content-aware sampling to enhance performance in tasks such as semantic segmentation, object detection, and depth estimation.
• Quantitative benchmarks show that DySample achieves competitive mIoU, AP, and PQ scores with lower FLOPs and latency, making it ideal for resource-constrained environments.

An Overview of "Learning to Upsample by Learning to Sample"

The paper "Learning to Upsample by Learning to Sample" introduces DySample, an ultra-lightweight, effective dynamic upsampler designed to address the limitations of existing kernel-based dynamic upsamplers like CARAFE, FADE, and SAPA. These prior methods, although performant, often incur high computational costs and limited applicability due to the need for high-resolution feature guidance and complex structures. By pivoting from kernel-based dynamic convolution to point sampling, DySample achieves higher efficiency and ease of integration with standard frameworks like PyTorch, while maintaining competitive performance across multiple dense prediction tasks.

Key Contributions and Methodology

1. Dynamic Sampling Approach:
   • DySample reformulates upsampling as a point sampling task rather than relying strictly on dynamic convolution. This is achieved by interpolating input features to generate continuous maps, and then resampling using content-aware sampling points. This method allows implementation using PyTorch’s built-in functions, circumventing the need for customized CUDA kernels.
2. Naive to Advanced Design:
   • The evolution from a naive linear offset generation to a sophisticated grouped dynamic scope offset generation is mapped out. Through several optimizations, including the use of initial sampling positions and modulation of offsets with static and dynamic scope factors, DySample improves efficiency and performance.
3. Performance Benchmarks:
   • DySample surpasses existing upsamplers in tasks such as semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation. Its performance gains are shown quantitatively with metrics like mIoU and bIoU for segmentation tasks, AP for object detection, PQ for panoptic segmentation, and various metrics for depth estimation.
4. Efficiency and Practicality:
   • Besides effectiveness, DySample is characterized by lower FLOPs, memory consumption, and latency compared to prior dynamic upsamplers. For example, using MaskFormer with Swin-Base, DySample achieves 53.91 mIoU on ADE20K with reduced computational overhead.

Implications and Future Directions

DySample’s approach to dynamic upsampling through efficient point sampling has significant implications for both academic research and practical applications. Its lightweight nature makes it an attractive choice for deploying high-performing models in resource-constrained environments, such as edge devices. From a theoretical standpoint, it inspires a reconsideration of dynamic networks to emphasize spatial information through geometric sampling rather than solely relying on increased convolutional complexity.

Future research could explore extending DySample's methodology into other domains requiring dynamic spatial adaptation, such as low-level image processing tasks or video frame interpolation. Additionally, continuing to refine the offset generation process to further reduce computational load while enhancing performance could yield even more efficient models.

In conclusion, DySample represents a substantial step forward in efficient feature upsampling, offering a flexible alternative to traditional methods with compelling gains in both performance and adaptability. Its design principles may well shape the future of dynamic network components, underscoring the value of simplicity and computational thrift in model architecture design.