arXiv Computer Vision
Learning Discriminative Prototypes via Boundary Repulsion MixUp for Object Re-Identification
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 17
CALIPER: Metric-Grounded Model-Free Recognition of Visually Similar Industrial Parts
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.
arXiv Computer Vision
Sep 11
Prototype Matters: Modality-unified Prototype Self-distillation for Unsupervised Visible-infrared Person Re-identification
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.
arXiv Computer Vision
Oct 19
ODSS-Net: Orthogonally Decoupled Shared-Specific Representations for Multi-Modal Object Re-Identification
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.
arXiv Machine Learning
Jun 2
Closing the Alignment-Maturity Gap in Federated Prototype Learning
arXiv:2606. 02172v1 Announce Type: new Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL).
arXiv AI
Oct 26
Complementing DINO Features with Image Structure for Part Discovery
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.
arXiv Computer Vision
Oct 16
Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware Approach
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.