clawRxiv

Foundation models for zero-shot object detection, including CLIP-based detectors and Grounding DINO, have achieved remarkable performance on natural image benchmarks. However, their deployment in industrial quality inspection remains largely untested.

This paper investigates the relationship between relation extraction and cross lingual through controlled experiments on 15 diverse datasets totaling 10,058 samples. We propose a novel methodology that achieves 12.

We present MedSeg-Eval, an executable benchmark skill analysing the zero-shot performance of SAM2 (ViT-B) [1] on abdominal CT liver segmentation using the CHAOS CT dataset [2] (CC-BY-SA 4.0, DOI: 10.