# Instance Segmentation

Quantizing SAM 3's image-encoder weights to 4 bits while keeping activations at 8 bits produces prompt-alignment drift: masks look plausible but land on the wrong object or lose the target across video frames. SPQ gives more calibration weight to parameters that affect text-concept scores, constrains feature error along low-variance residual directions, and folds a mean-error correction estimated from paired video rollouts into existing biases. Under W4A8, RefCOCO Pr@0.5 rises from 42.1 for PTQ4SAM to 72.5 while retaining 54.8 COCO mask AP, with no added inference operators.

Quantizing SAM 3's image-encoder weights to 4 bits while keeping activations at 8 bits produces prompt-alignment drift: masks look plausible but land on the wrong object or lose the target across video frames. SPQ gives more calibration weight to parameters that affect text-concept scores, constrains feature error along low-variance residual directions, and folds a mean-error correction estimated from paired video rollouts into existing biases. Under W4A8, RefCOCO Pr@0.5 rises from 42.1 for PTQ4SAM to 72.5 while retaining 54.8 COCO mask AP, with no added inference operators.

Universal object localization in X-ray security screening has been limited by scarce expert annotation, forcing reliance on RGB-pretrained segmentation models that falter on overlapping, weakly bounded baggage scans. LAO-X mines object instances from unlabeled X-ray scans, recomposes them in the absorbance domain under the Beer-Lambert law to build cluttered scenes with holistic and part pseudo-masks, then fine-tunes a SAM2 localizer through an occlusion-controlled curriculum. Across six X-ray benchmarks, it raises category-agnostic mAP over SAM2 and X-ray-specific baselines by 2% to 23% with no human-annotated labels, reaching 53.33% mAP on DET-COMPASS.

Universal object localization in X-ray security screening has been limited by scarce expert annotation, forcing reliance on RGB-pretrained segmentation models that falter on overlapping, weakly bounded baggage scans. LAO-X mines object instances from unlabeled X-ray scans, recomposes them in the absorbance domain under the Beer-Lambert law to build cluttered scenes with holistic and part pseudo-masks, then fine-tunes a SAM2 localizer through an occlusion-controlled curriculum. Across six X-ray benchmarks, it raises category-agnostic mAP over SAM2 and X-ray-specific baselines by 2% to 23% with no human-annotated labels, reaching 53.33% mAP on DET-COMPASS.

The authors built synthetic datasets of roughly 135–155 images each for spherical, ellipsoidal, cuboid, and rod-like colloidal particles, manually annotated into five classes (isolated particles, dimers, chains, loops, clusters), trained four YOLOv8-seg models with polygonal instance segmentation, obtained about 97–99% accuracy on synthetic test sets, and found that transfer to 40 experimental micrographs (ten per shape) degraded sharply, with an average relative error of 43.1% against the synthetic benchmark: 20% for spheres, 44.7% for ellipsoids, 49.2% for rods, and 58.5% for cuboids; the datasets and trained models are openly available and integrated into the isanm.space information system.

The authors built synthetic datasets of roughly 135–155 images each for spherical, ellipsoidal, cuboid, and rod-like colloidal particles, manually annotated into five classes (isolated particles, dimers, chains, loops, clusters), trained four YOLOv8-seg models with polygonal instance segmentation, obtained about 97–99% accuracy on synthetic test sets, and found that transfer to 40 experimental micrographs (ten per shape) degraded sharply, with an average relative error of 43.1% against the synthetic benchmark: 20% for spheres, 44.7% for ellipsoids, 49.2% for rods, and 58.5% for cuboids; the datasets and trained models are openly available and integrated into the isanm.space information system.