arXiv AI June 16, 2026 By Yan Jiao, Pin-Han Ho, Limei Peng

Sensor-Conditioned Representation Learning via Scene-Relevant Observation Quotients

arXiv:2606. 16210v1 Announce Type: new Abstract: Learned representations in intelligent sensing systems are often evaluated by reconstruction fidelity or downstream prediction accuracy, but these criteria do not specify which latent distinctions are justified by the sensing process.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Jun 8

Conformal Disentanglement and Latent-Space Curation: A Neural Framework for Perspective Synthesis, Differentiation and Targeted Generation

arXiv:2408. 15344v2 Announce Type: replace Abstract: Many scientific and engineering problems involve observing a common phenomenon through multiple heterogeneous sensors or measurement modalities.

By George A. Kevrekidis, Eleni D. Koronaki, Dimitris G. Giovanis, Yannis G. Kevrekidis

arXiv AI

Jul 1

Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation

arXiv:2606. 31609v1 Announce Type: cross Abstract: Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements make dense semantic segmentation challenging.

By Ali Zia, Muhammad Umer Ramzan, Abdelwahed Khamis, Usman Ali, Abdul Rehman

computer-vision efficiency benchmarks safety

arXiv AI

Jun 4

The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning

arXiv:2606. 04280v1 Announce Type: cross Abstract: Contrastive learning has become a leading paradigm for self-supervised representation learning, yet the conditions under which it recovers meaningful latent geometry remain incompletely understood.

By Justinas Zaliaduonis, Patrick Putzky, Till Richter, Sergios Gatidis

diffusion benchmarks safety

arXiv Machine Learning

Jul 28

EditCLEVR: A Paired-Scene Intervention Benchmark for Compositional Faithfulness of Object-Centric Representations

arXiv:2607. 22705v1 Announce Type: cross Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations.

By Anuraag Gadehothur Karnam, Tarunesh Sathish

computer-vision benchmarks

arXiv AI

1d ago

Hierarchical Adaptive Feature Refinement Network for VHR Remote Sensing Image Segmentation

arXiv:2608. 15647v1 Announce Type: cross Abstract: Semantic segmentation of very-high-resolution (VHR) remote sensing imagery increasingly benefits from strong pretrained hierarchical encoders, yet exploiting their multi-stage representations remains difficult.

By Shuaishuai Cao, Meng Tang, Shuwei Peng, Xuan Liu, Min Huang, Jie Chen, Jiacheng Niu, Yong Chen, Edore Akpokodje, Hui Lin

computer-vision

arXiv Machine Learning

Jul 3

Rethinking Post-Hoc Calibration in Semantic Segmentation

arXiv:2607. 01902v1 Announce Type: cross Abstract: Reliable confidence estimates are essential in semantic segmentation, especially in safety-critical settings where overconfident errors can mislead downstream decisions.

By Tristan Kirscher (ICube), Kim-Celine Kahl (DKFZ), Balint Kovacs (DKFZ), Maximilian R. Rokuss (DKFZ), Klaus Maier-Hein (DKFZ), Xavier Coubez (ICube), Philippe Meyer (ICube), Sylvain Faisan (ICube)

computer-vision benchmarks safety

Related stories

Conformal Disentanglement and Latent-Space Curation: A Neural Framework for Perspective Synthesis, Differentiation and Targeted Generation

Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation

The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning

EditCLEVR: A Paired-Scene Intervention Benchmark for Compositional Faithfulness of Object-Centric Representations

Hierarchical Adaptive Feature Refinement Network for VHR Remote Sensing Image Segmentation

Rethinking Post-Hoc Calibration in Semantic Segmentation