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arXiv:2610.41358v1 Announce Type: new Abstract: Existing visual preference evaluation relies on costly human annotations or preference reward models, whereas online marketplace transaction prices are...
arXiv:2609.15100v1 Announce Type: cross Abstract: An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study t...
arXiv:2609.13279v1 Announce Type: new Abstract: Fashion attribute extraction is evaluated inconsistently: results are reported as single aggregate numbers across image types that pose different probl...
arXiv:2609.13308v1 Announce Type: cross Abstract: A companion evaluation found that naming the target part in a manipulation prompt increased action accuracy by 0.32-0.63 across eight vision-language...
DICL-Qwen3VL is a decoupled, interpretable vision‑language model that localizes upper‑body clothing and identifies workwear in indoor construction sites. It uses low‑rank adaptation of Qwen3‑VL, separates visual and reasoning confidence estimates, and employs token entropy and image perturbation sensitivity to supervise visual confidence. Experiments on the Indoor Construction Site Workwear Dataset and the web‑collected Indoor Construction Workwear Dataset achieve mAP@50 scores of 0.730 and 0.753, outperforming baseline VLMs and providing explicit evidence and confidence scores for industrial safety monitoring.
arXiv:2610.27192v1 Announce Type: cross Abstract: Multimodal large language models (LLMs) are increasingly used for structured extraction from financial documents, but their behavior under visual amb...