The paper introduces STITCH, a self‑supervised compatibility measure that replaces traditional boundary‑dependent metrics for solving visual puzzles with eroded fragments. By comparing frozen ViT patch tokens from piece interiors using a lightweight cross‑attention head, STITCH can be plugged into existing solvers and achieves near‑perfect reconstruction on the JPwLEG‑5 benchmark, outperforms prior methods on a new ImageNet 7×7 benchmark, and remains robust across varying erosion levels and high missing‑piece scenarios. The authors demonstrate that improving the matching metric, rather than the solver itself, yields significant performance gains across multiple datasets.
By Nadav Alali, Ohad Ben-Shahar
The paper introduces an AI-assisted pipeline for retrieving goldsmith marks from silverware images, combining mark localization with metric‑learning fine‑tuning on three backbone models (ResNet‑50, ViT‑S/16, and DINOv2 ViT‑S/14). Systematic tests of cropping strategies show that manual cropping and metric‑learning fine‑tuning yield the best performance, with DINOv2 ViT‑S/14 achieving 62.63% mAP and 73.74% Top‑1 accuracy. The authors release a manually annotated dataset, code, and a public web interface to support reproducibility and adoption in digital humanities.
By Atmik Tiwari, Vincent Christlein, Mark Fichtner, Freya Gohlke, Birgit Sch\"ubel, Theresa Witting, Heike Zech, Mathias Zinnen
arXiv:2611.05408v1 Announce Type: new
Abstract: Low-textured sketch images are widely used in scientific applications, including wildlife conservation, archaeological illustration, and forensic compo...
By Zhiqiang Wang, Sheri L. Barton, Jennifer H. Johnson, Yi He, Xingquan Zhu
arXiv:2604.22868v2 Announce Type: replace-cross
Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation....
By Zhimu Zhou, Yanpeng Zhao, Qiuyu Liao, Bo Zhao, Xiaojian Ma
arXiv:2609.07937v1 Announce Type: cross
Abstract: Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, an ability current Vision Language Models (VLMs) lack. VL...
By Harsha Patnala, Debopriyo Banerjee, Ayush Sunil Munot, Somak Aditya
arXiv:2609.21012v1 Announce Type: cross
Abstract: Artistic style classification is usually studied on complete artworks, where models can exploit global composition, spatial organisation, and iconogr...
By Sara Miketek, Biagio Barchielli, Nadeem Iqbal Kajla, Sinem Aslan
RAGDiffusion++ advances garment generation by addressing the high‑frequency texture gap that previous retrieval‑augmented models left unresolved. The approach introduces a dual‑image FLUX architecture trained on a large, complex garment dataset, coupled with a new attribute‑aware reward model that guides reinforcement learning to favor realistic high‑frequency patterns. An adversarial‑regularized RL strategy (AR‑GRPO) further prevents artifact exploitation, ensuring the model samples authentic, detailed garment textures.
By Yuhan Li, Xianfeng Tan, Fangao Zeng, Wenxiang Shang, Pipei Huang, Hao Zhou, Zhiyu Jin, Wenjun Zhang, Bingbing Ni
arXiv:2609.14473v1 Announce Type: new
Abstract: Personal AI assistants hold the potential to evolve from digital interfaces into embodied companions capable of guiding users through complex physical...
By Avijit Dasgupta, Shayon Dasgupta, Zakaria Laskar, C. V. Jawahar, Karteek Alahari
arXiv:2610.15582v1 Announce Type: new
Abstract: We study the problem of inverse garment design, reconstructing simulation-ready 3D outfits with arbitrary layers from a single image, with visual agent...
By Nanxiang Jiang, Yunuo Chen, Zeng Tao, Zizhang Li, Qi Guo, Jiajun Wu, Yin Yang, Chenfanfu Jiang
arXiv:2511. 09483v3 Announce Type: replace Abstract: While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored.
By Peiyu Li, Xiaobao Huang, Ting Hua, Nitesh V. Chawla
arXiv:2610.19623v1 Announce Type: new
Abstract: Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layere...
By Jia Chen, Ruyang Zhao, Jun Bai, Chao Huang, Saishang Zhong, Xinrong Hu
arXiv:2606. 03273v2 Announce Type: replace-cross Abstract: Visual DeepSearch tasks require multimodal large language models (MLLMs) to resolve complex visual queries by repeatedly inspecting image regions, grounding reasoning in visual evidence, and connecting fine-grained clues across multiple steps.
By Hang He, Chuhuai Yue, Chengqi Dong, Chengcheng Wan, Ting Su, Haiying Sun, Jiajun Chai, Xiaohan Wang, Guojun Yin