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During multi-scale testing, features from two adjacent scales of the image pyramid are pooled and merged using maxout layers.
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.4 Residual Network Experiments and Applications - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Object Detection and Localization using Residual Networks - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Related
In the ResNet-based detection pipeline, how is the global context feature extracted and integrated with region-level features?
During multi-scale testing, features from two adjacent scales of the image pyramid are pooled and merged using maxout layers.
In the iterative box refinement process, what IoU threshold is applied during non-maximum suppression (NMS) when combining the 300 regressed predictions with the initial proposals?
Describe the ensembling strategy used with the ResNet-101 Faster R-CNN system, and state the benchmark performance results and placement achieved by this ensemble on MS COCO test-dev.