CALIPER is a model‑free RGB‑D framework that performs fine‑grained recognition of visually similar industrial parts by combining support‑based appearance matching with metric size evidence. Each class is onboarded from a single turntable RGB‑D video and a few labeled real images, enabling 3D reconstruction for appearance support and depth‑aligned size profiling. At inference, a YOLOv8n‑seg model localizes parts, a frozen DINOv2 backbone with an episodically trained embedding head matches support, and margin‑conditioned metric fusion selectively uses size evidence for ambiguous cases, achieving high accuracy on 18 parts and robust enrollment of unseen screws without retraining.
By Alankrit Gupta, Chenxi Tao, Seung-Kyum Choi
The paper introduces a new framework for unsupervised visible‑infrared person re‑identification that leverages modality‑unified prototypes. By contrasting with prototypes that unify both modalities, the method jointly optimizes similarity within and across modalities, improving modality invariance. A self‑distillation step refines instance‑prototype relationships using a steady teacher, resulting in a simple yet effective model validated on standard VI‑ReID benchmarks.
By Menglin Wang, Xiaojin Gong
ODSS-Net introduces a framework for multi‑modal object re‑identification that separates shared structural information from modality‑specific cues. It employs a Shared Structural Feature Construction module to extract modality‑shared structure, an Orthogonal Specific Feature Learning module to isolate identity‑relevant local features, and a Sample Adaptive Mixture of Experts module to fuse these features based on shared structure. Experiments on three benchmarks confirm the effectiveness of this orthogonally decoupled representation learning approach.
By Zhifeng Ma, Yuhong Feng, Chongyi Huang, Mingyang Zhou, Minwen Deng
arXiv:2606. 02172v1 Announce Type: new Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL).
By Mario Casado-Diez, Alejandro Dopico-Castro, Ver\'onica Bol\'on-Canedo, Bertha Guijarro-Berdi\~nas
The paper introduces Part‑DINO, a training‑free framework that enhances self‑supervised Vision Transformer (ViT) features from DINO with low‑level image boundaries to improve part discovery. By partitioning images using boundary detectors, pooling DINO features within each region, and clustering them, Part‑DINO yields structurally coherent part assignments. It outperforms other training‑free baselines on four standard benchmarks and remains competitive with weakly‑supervised and unsupervised methods.
By Samyak Rawlekar, Nikhil Chowdary Paleti, Amey Gupta, Narendra Ahuja
The paper introduces a self‑supervised, geometry‑aware method that refines DINOv3 features with LoRA to better handle partial 3D shapes. By training on partial observations, the resulting descriptors outperform traditional and other vision‑foundation features in partial shape matching tasks. When incorporated into existing pipelines, the approach achieves state‑of‑the‑art results on both partial shape matching and left‑right prediction benchmarks.
By Viktoria Ehm, Dongliang Cao, Riccardo Marin, Daniel Scholz, Weikang Wang, Florian Bernard, Daniel Cremers
arXiv:2609.21522v1 Announce Type: new
Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representation...
By Hang Cheng, Yan Chen, Mingyu Fan, Long Zeng
arXiv:2603.11521v2 Announce Type: replace-cross
Abstract: Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and t...
By Shuo Jiang, Gaojia Zhang, Min Tan, Yufei Yin, Gang Pan
arXiv:2607.05568v2 Announce Type: replace-cross
Abstract: Compact primitive abstractions represent 3D shapes with a few geometric primitives while preserving recognizable components. Learned methods...
By Gregor Kobsik, Tim Elsner, Leif Kobbelt
arXiv:2608.29733v1 Announce Type: new
Abstract: Visual aliasing, also known as the doppelganger problem, remains a key challenge for structure-from-motion (SfM): visually similar but physically disti...
By Gonglin Chen, Ben Southall, Hanyuan Xiao, Wenbin Teng, Haolin Xiong, Tianwen Fu, Junyi Ouyang, Kshitij Singh Minhas, Supun Samarasekera, Rakesh Kumar, Yajie Zhao
arXiv:2609.36560v1 Announce Type: cross
Abstract: Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that di...
By Zhiqi Li, Xiaowei Zhou, Zeyuan Sun, Feng Gao, Junyu Dong
arXiv:2608. 19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes.
By Shangbo Yuan, Jie Xu, Xiaofeng Zhu, Na Zhao