Brief Introduction

• Indicate the "essential difference" between anchor-based and anchor-free detectors
  • How to define positive and negative training samples
• Propose an "adaptive training sample selection" to automatically select positive and negative training samples
• Demonstrate tiling multiple anchors per location is useless
• Achieve SOTA performance on MS COCO without overhead

Recent vision models

anchor-based & anchor-free

• Mainly two methods for vision processing
  • Anchor-based method: single-stable & double-stage
  • Anchor-free method: keypoint-based & center-based

Anchor-based

• Double-stage: Faster R-CNN
  • Consists of Region Proposal Network(RPN) & region-wise prediction network (R-CNN)
  • Good accuracy with anchor refinement
• Single-stage: Single Shot Detection (SSD)
  • High computational efficiency

Anchor-free

• Keypoint-based method: CornerNet
  • First locates several pre-defined or self-learned keypoints
  • Then, generates bounding boxes
• Center-based method: YOLO
  • Regards the center of object as foreground to define positives
  • Then, predicts the distances from positive to the four sides of the object bounding box

Difference Analysis between two

• RetinaNet (anchor-based) vs. FCOS (anchor-free)
  • one-stage anchor-based & center-based anchor-free
• Attention points
  • The positive/negative sample definition
  • The number of anchors tiled per location
• Dataset: MS COCO (80 object classes)
• RetinaNet (#A=1) → one square anchor box per location

Inconsistency removal