← Back to all news
arXiv Machine Learning By Haolu Liu, Xiyue Wang, Xuanting Xie, Liangjian Wen, Zhao Kang

Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence

Read the original on arXiv Machine Learning →

The paper introduces the concept of protocol divergence, showing that identical nominal missing rates can lead to vastly different learning regimes in incomplete multi‑view clustering. It critiques existing evaluation practices that ignore observation structure and proposes CRAFT, a train‑once framework that fuses observed views with mask‑aware attention, enabling efficient deployment across multiple missing‑view protocols. Experiments on CUB, MultiFashion, and other benchmarks demonstrate CRAFT’s superior performance and significant computational savings through checkpoint reuse.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Computer Vision
Aug 27

See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection

The paper identifies a problem in multi‑view anomaly detection called cross‑view information leakage, where fusing multiple inspection views can cause normal features to mask anomalies during reconstruction. To address this, the authors propose GLAD, a framework that uses a Global‑Local Attention Driven approach, combining vision foundation model features with two fusion modules: Multi‑view Merging Attention for local, weighted fusion and Object‑Guided Attention for global context aggregation. Experiments on Real‑IAD and MANTA‑Tiny demonstrate that GLAD outperforms existing methods across various metrics, underscoring the importance of restricting information flow to preserve the reconstruction gap.

By Shang-Fu Chen, Kuan-Chuan Peng, Jhih-Ciang Wu, Wen-Huang Cheng, Kai-Lung Hua
benchmarks
More like this →
arXiv Machine Learning
Sep 23

SAM-V: Geometry-Aware Segment Anything for Multi-View Instance Segmentation

SAM‑V is a geometry‑aware extension of the Segment Anything Model (SAM) that integrates 3D priors from a feed‑forward geometry model (VGGT) into 2D segmentation. It uses a prompt‑fusion mechanism to combine sparse SAM prompts with view‑specific camera tokens and local VGGT features, enabling a mask decoder that attends to both dense 2D and 3D cues. The resulting end‑to‑end system produces consistent multi‑view instance segmentation in a single forward pass, achieving significant gains on the IGGT 3D tracking benchmark without offline mask matching or explicit 3D reconstruction.

By Jiangshan Gong, Yuqun Wu, Qiqian Fu, Yao Xiao, Chuhang Zou, Shenlong Wang, Derek Hoiem
computer-visionroboticsbenchmarks
More like this →
arXiv Machine Learning
Jul 14

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification

arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.

By Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig
llmsdiffusion
More like this →