Deformable ConvNets v2: More Deformable, Better Results Deformable Convolutional Networks (DCN) excel in adapting to object geometry. However, their spatial support may extend beyond the region of interest, affecting features from irrelevant areas. To address this, DCNv2 improves focus on relevant regions through enhanced modeling and training. It integrates deformable convolution more comprehensively and introduces a modulation mechanism for deformation modeling. A feature mimicking scheme guides training to learn features similar to R-CNN. DCNv2 outperforms DCN on COCO benchmarks for object detection and instance segmentation. DCNv2 enhances modeling power by using more deformable convolution layers and a modulation mechanism. This allows better control of sampling and pooling patterns. Training uses a teacher network with R-CNN for feature mimic loss, improving focus on object regions. DCNv2 is lightweight and integrates into existing architectures like Faster R-CNN and Mask R-CNN. Experiments show significant improvements over DCNv1 on COCO. Analysis of DCNv2 shows better spatial support adaptation. Visualizations reveal that DCNv2's effective sampling locations, receptive fields, and saliency regions provide more insight than previous methods. Deformable ConvNets adapt spatial support more to image content, with foreground nodes covering whole objects and background nodes including irrelevant areas. The three visualization modalities offer more information than sampling locations alone. DCNv2 improves by stacking more deformable conv layers and modulating deformable modules. Modulation allows adjusting feature amplitudes from different spatial locations. This provides more flexibility in spatial support. The design of modulated deformable RoIpooling includes learnable offsets and modulation scalars, enhancing performance. R-CNN feature mimic loss helps DCNv2 learn features similar to R-CNN, improving focus on object regions. This is enforced on positive RoIs, enhancing detection accuracy. The network architecture includes an additional R-CNN branch for feature mimic loss, improving performance without significant computational overhead. Experiments show DCNv2 outperforms DCNv1 on COCO benchmarks. Enriched deformation modeling improves accuracy, with DCNv2 achieving higher AP scores. R-CNN feature mimic loss further improves performance, especially for positive boxes. DCNv2 performs well on various backbones, including ResNet-101 and ResNext-101. DCNv2's spatial support adapts better to image content, improving detection accuracy. The reformulated DCNv2 enhances modeling power and training, leading to significant performance gains on COCO. The paper concludes that DCNv2 improves focus on relevant image regions, achieving better results on object detection and instance segmentation.Deformable ConvNets v2: More Deformable, Better Results Deformable Convolutional Networks (DCN) excel in adapting to object geometry. However, their spatial support may extend beyond the region of interest, affecting features from irrelevant areas. To address this, DCNv2 improves focus on relevant regions through enhanced modeling and training. It integrates deformable convolution more comprehensively and introduces a modulation mechanism for deformation modeling. A feature mimicking scheme guides training to learn features similar to R-CNN. DCNv2 outperforms DCN on COCO benchmarks for object detection and instance segmentation. DCNv2 enhances modeling power by using more deformable convolution layers and a modulation mechanism. This allows better control of sampling and pooling patterns. Training uses a teacher network with R-CNN for feature mimic loss, improving focus on object regions. DCNv2 is lightweight and integrates into existing architectures like Faster R-CNN and Mask R-CNN. Experiments show significant improvements over DCNv1 on COCO. Analysis of DCNv2 shows better spatial support adaptation. Visualizations reveal that DCNv2's effective sampling locations, receptive fields, and saliency regions provide more insight than previous methods. Deformable ConvNets adapt spatial support more to image content, with foreground nodes covering whole objects and background nodes including irrelevant areas. The three visualization modalities offer more information than sampling locations alone. DCNv2 improves by stacking more deformable conv layers and modulating deformable modules. Modulation allows adjusting feature amplitudes from different spatial locations. This provides more flexibility in spatial support. The design of modulated deformable RoIpooling includes learnable offsets and modulation scalars, enhancing performance. R-CNN feature mimic loss helps DCNv2 learn features similar to R-CNN, improving focus on object regions. This is enforced on positive RoIs, enhancing detection accuracy. The network architecture includes an additional R-CNN branch for feature mimic loss, improving performance without significant computational overhead. Experiments show DCNv2 outperforms DCNv1 on COCO benchmarks. Enriched deformation modeling improves accuracy, with DCNv2 achieving higher AP scores. R-CNN feature mimic loss further improves performance, especially for positive boxes. DCNv2 performs well on various backbones, including ResNet-101 and ResNext-101. DCNv2's spatial support adapts better to image content, improving detection accuracy. The reformulated DCNv2 enhances modeling power and training, leading to significant performance gains on COCO. The paper concludes that DCNv2 improves focus on relevant image regions, achieving better results on object detection and instance segmentation.