ECCV 2026｜国防科大提出SPIRE：红外小目标检测新突破

TL;DR — NUDT's Prof. Wei An group has an ECCV 2026 paper, SPIRE, that reframes infrared small target detection (IRSTD) from pixel-level segmentation to single-point-supervised probabilistic centroid regression, cutting annotation, compute, and post-processing cost while staying competitive on target-level accuracy.

• Pipeline: centroid point labels → Point-Response Prior Supervision (PRPS) expands each point into a smooth local probability response modulated by infrared radiation peak and local contrast → High-Resolution Probabilistic Encoder (HRPE), a single-branch encoder-only net with no decoder/skip connections → local peak extraction + sub-pixel refinement gives coordinates directly (no mask, no connected-component clustering).
• Efficiency: 0.29M params, 7.68G FLOPs at 640×640, 261.2 FPS — vs SCTransNet, ~87.9% fewer FLOPs and ~97.4% fewer params, and reported 3.2–5.6× faster than three high-accuracy baselines.
• Accuracy under a unified centroid protocol (hit if predicted center within 5 px): SIRST-UAVB Precision 99.82%, Fa 1.02×10⁻⁸ (both best), F1 97.05% (0.04 pt off top), Recall 94.44% (not top); SIRST4 best on all four metrics (P 95.00%, R 94.21%, F1 94.60%, Fa 28.53×10⁻⁸), and highest F1 under stricter δ=3.
• Ablations: PRPS beats single-point impulse supervision by +2.78 F1 and unconstrained Gaussian by +0.77 F1; enlarging output stride drops F1 by >10 pts while further upsampling costs ~3.5× FLOPs with no F1 gain, and removing channel shuffle/reweighting raises false alarms. Code: NIRIXIANG/SPIRE-IRSTD; paper listed as arXiv:2604.05363.