Deformable ConvNets v2: More Deformable, Better Results (1811.11168v2)

Abstract: The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training. The modeling power is enhanced through a more comprehensive integration of deformable convolution within the network, and by introducing a modulation mechanism that expands the scope of deformation modeling. To effectively harness this enriched modeling capability, we guide network training via a proposed feature mimicking scheme that helps the network to learn features that reflect the object focus and classification power of R-CNN features. With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading results on the COCO benchmark for object detection and instance segmentation.

• The paper introduces a dual enhancement approach by extending deformable layers to earlier stages and integrating a novel modulation mechanism.
• The paper employs feature mimicking with an R-CNN teacher to improve object-centric focus and reduce background noise.
• The paper demonstrates significant performance gains, achieving up to 43.1% AP on Mask R-CNN with the COCO benchmark.

Deformable ConvNets v2: More Deformable, Better Results

Deformable Convolutional Networks (DCNv1) have been recognized for their ability to adapt to the geometric variations inherent in objects, leading to substantial improvements in object recognition and detection tasks. This paper introduces an enhanced version, Deformable ConvNets v2 (DCNv2), which addresses some limitations of the original model and achieves remarkable performance gains on challenging benchmarks like COCO.

Enhancements in Modelling Power

DCNv2 brings significant improvements through two main enhancements: increased integration of deformable convolution layers and a new modulation mechanism.

1. Expanded Use of Deformable Layers: By integrating deformable convolution layers not only in the conv5 stage, as in DCNv1 but also in the conv3 and conv4 stages, the authors achieve more robust modeling of geometric transformations. This extended use enriches the network's ability to handle variations across different feature levels.
2. Modulation Mechanism: The new modulation mechanism in DCNv2 allows each sample to undergo a learned offset, modulated by a learned feature amplitude. This dual adjustment capability enables the network to selectively focus on relevant image regions and adapt the influence of different samples, enhancing the descriptive power of the features.

Effective Training via Feature Mimicking

To fully exploit the increased modeling capacity, DCNv2 incorporates a feature mimicking scheme inspired by knowledge distillation techniques. By using R-CNN as a teacher network, the DCNv2 network learns to emulate features that reflect the object-centric focus and classification robustness of R-CNN features. This guidance ensures that DCNv2 can effectively leverage its enriched deformable sampling capabilities without being adversely affected by irrelevant background content.

Experimental Results and Implications

The paper reports extensive experimental results that underscore the effectiveness of DCNv2. When tested on the COCO benchmark, DCNv2 consistently outperforms DCNv1 and regular ConvNets, demonstrating significant improvements in object detection and instance segmentation tasks. For instance, DCNv2 achieves a 41.7% AP with Faster R-CNN and a 43.1% AP with Mask R-CNN, which surpasses corresponding results of 38.0% and 40.4% obtained with DCNv1.

Interestingly, the introduction of the modulation mechanism translates to gains in AP by 0.3-0.7%, while stacking more deformable convolution layers results in gains of 2.0-3.0%. These numerical results highlight the beneficial impact of the enhanced deformation modeling power.

Theoretical and Practical Implications

Theoretically, DCNv2's success suggests that augmenting convolutional neural networks with more flexible and adaptive sampling mechanisms can substantially improve their alignment with object structures. This could pave the way for future research focusing on advanced dynamic sampling techniques that further enhance model robustness to geometric variations.

Practically, the improvements in object detection and instance segmentation underscore the utility of DCNv2 in real-world applications, such as autonomous driving, surveillance, and medical imaging, where accurately detecting and segmenting objects under various transformations is crucial.

Future Developments

Future research could explore further augmentation of deformable networks with more sophisticated modulation mechanisms and deeper integration across different stages of the network. Additionally, exploring other avenues of network guidance, beyond feature mimicking, might provide new ways to steer training towards more effective feature representations.

Conclusion

This paper effectively demonstrates how enhancing the deformability and training methodologies of convolutional networks can lead to noticeable improvements in performance. DCNv2's increased ability to focus on pertinent image regions, made possible by its enriched modeling power and feature mimicking training, establishes it as a significant step forward from DCNv1, cementing its utility in a variety of complex vision tasks.