ASFF: Adaptive Spatial Feature Fusion
Learning where to fuse multi-scale features with per-pixel, per-level fusion weights. ASFF challenges FPN's uniform fusion assumption.
Explore machine learning concepts related to object detection. Clear explanations and practical insights.
Learning where to fuse multi-scale features with per-pixel, per-level fusion weights. ASFF challenges FPN's uniform fusion assumption.
Understanding region-based feature extraction for object detection, from quantized pooling to sub-pixel alignment and adaptive sampling
Compare anchor-based vs anchor-free object detection: Faster R-CNN and RetinaNet anchors vs FCOS and CenterNet point-based methods.
Understanding how neural architecture search discovers optimal feature pyramid architectures that outperform hand-designed alternatives
Understanding end-to-end object detection with transformers, from DETR's object queries to bipartite matching and attention-based localization
Understanding Non-Maximum Suppression algorithms for object detection post-processing, from greedy NMS to soft variants