PnP-DETR: Towards Efficient Visual Analysis with Transformers (2109.07036v4)

Abstract: Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result. Though effective, translating the full feature map can be costly due to redundant computation on some area like the background. In this work, we encapsulate the idea of reducing spatial redundancy into a novel poll and pool (PnP) sampling module, with which we build an end-to-end PnP-DETR architecture that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The transformer models information interaction within the fine-coarse feature space and translates the features into the detection result. Moreover, the PnP-augmented model can instantly achieve various desired trade-offs between performance and computation with a single model by varying the sampled feature length, without requiring to train multiple models as existing methods. Thus it offers greater flexibility for deployment in diverse scenarios with varying computation constraint. We further validate the generalizability of the PnP module on panoptic segmentation and the recent transformer-based image recognition model ViT and show consistent efficiency gain. We believe our method makes a step for efficient visual analysis with transformers, wherein spatial redundancy is commonly observed. Code will be available at \url{https://github.com/twangnh/pnp-detr}.

• The paper introduces a novel Poll and Pool module that abstracts image features into fine and coarse vectors to reduce spatial redundancy.
• It dynamically allocates computation to focus on informative foreground areas, achieving a 72% reduction in transformer processing on benchmark tests.
• The approach generalizes to tasks like panoptic segmentation, offering adaptable performance for resource-constrained deployments.

Analysis of "Sampling DETR: Efficient End-to-End Object Detection with Spatially Adaptive Sampling"

The evolution and efficacy of object detection models hold substantial importance in the field of computer vision, particularly when leveraging transformer architectures. The paper "Sampling DETR: Efficient End-to-End Object Detection with Spatially Adaptive Sampling" presents a nuanced approach to addressing spatial redundancy in DETR models. This work builds on the foundation of the Detection Transformer (DETR), a pioneering endeavor in applying transformers to object detection tasks, and introduces a Poll and Pool (PnP) module aimed at optimizing computation cost without degrading model performance.

Core Contributions

1. Feature Abstraction Through PnP Module: The authors introduce a Poll and Pool (PnP) sampling module that effectively abstracts the image feature map. This abstraction results in two key components: fine feature vectors and coarse background contextual feature vectors. The fine feature vectors are crucial for identifying object features, while the coarse ones encapsulate background information. This separation allows for reduced spatial redundancy by selectively processing more informative parts of the feature map.
2. Dynamic Computation Allocation: The architecture, termed PnP-DETR, leverages this abstraction to dynamically allocate computation spatially throughout the image. Such dynamic computation adaptation aims to focus computational resources on foreground objects more intensely than on less informative background areas, thus improving processing efficiency.
3. Transforming Transformer Efficiency: An intriguing aspect of the proposed method is that it allows for varied trade-offs between computational resources and detection performance with minimal adjustments. By tuning parameters such as the sampled feature length, PnP-DETR can flexibly adapt to diverse deployment scenarios, an advantage over traditional models that require multiple trained models to achieve similar versatility.
4. Broad Applicability: Aside from object detection, the paper evaluates the PnP module on panoptic segmentation and transformer-based image recognition models like ViT, demonstrating its generalizability and consistent efficiency gains.

Numerical and Performance Insights

Experiments conducted on the COCO benchmark reveal that PnP-DETR can achieve comparable performance to its baseline with a significant reduction in computational demand. For instance, a PnP-DETR with a ResNet-50 backbone achieved a 42.7 AP with a 72% reduction in transformer computation compared to the non-augmented DETR model. Moreover, the ability to control the computation versus performance trade-off through the poll ratio adjustment without retraining unveils a promising pathway for real-time applications.

Implications and Future Directions

The proposed PnP module not only augments the computational efficiency of detection transformers but also opens avenues for future research in adaptive vision systems. This approach's potential simplification of memory and resource management can enable deployment in resource-constrained environments, like mobile platforms or edge computing scenarios. Moreover, this form of adaptive computation highlights transformers’ latent potential to emulate biological neural networks' adaptability more closely.

The fusion of efficient computing strategies, as displayed through PnP-DETR, affirms the broader implications of tailoring neural networks to process spatial data asymmetrically, leading towards more contextually aware and resource-efficient AI models. As computational demands and data grow exponentially, such nuanced adaptations will become pivotal in maintaining momentum in AI's development.

In conclusion, while the proposed enhancements primarily focus on reducing computation, the explorations and methodologies introduced pave the way for a more adaptive and optimized application of transformers in vision tasks. This reinforces the continuing evolution of transformers from a state-of-the-art to a staple in diverse AI applications.