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.
Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
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