arXiv AI July 20, 2026 By Stefano Silvestrini, Michele Ceresoli

On the Geometry of Learned Representations in Event-Based Multi-Modal Egomotion Estimation

arXiv:2607. 15794v1 Announce Type: cross Abstract: Classical approaches to event-based egomotion estimation, including those adopted by the top-performing teams of the ELOPE challenge, rely on geometric optimization frameworks such as contrast maximization, homography estimation, or dense optical flow combined with analytic motion inversion.

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arXiv Computer Vision

Aug 27

Moving Beyond More Views: Redundancy-Aware Ego-Exo Fusion for Proficiency Estimation

The paper introduces a redundancy-aware fusion framework for EgoExo proficiency estimation, which integrates fine-grained motion cues from egocentric views with spatial context from exocentric views. It identifies multiview redundancy and overfitting as key challenges and proposes two modules—AdaMVS for adaptive view selection and VIB-GB for compressing redundant signals—to address them. Experiments on EgoExo-4D and EgoExo-Fitness show that the method learns to select informative views and fuse them effectively, achieving state‑of‑the‑art results.

By Xu Dong, Wanqing Li, Anthony Adeyemi-Ejeye, Andrew Gilbert

benchmarks

Hugging Face Trending Papers

Jul 21

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention

Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.

multimodal

arXiv Machine Learning

Jul 2

Local Motion Matters: A Deconstruct-Recompose Paradigm for Reinforcement Learning Pre-training from Videos

arXiv:2607. 00808v1 Announce Type: new Abstract: Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging.

By Jinwen Wang, Youfang Lin, Xiaobo Hu, Shuo Wang, Kai Lv

agents reinforcement-learning robotics

arXiv Computer Vision

Sep 23

End-to-End Visual Odometry with RNNs and Attention

The paper "End-to-End Visual Odometry with RNNs and Attention" presents a study of deep‑learning approaches to visual odometry (VO), proposing a novel temporal attention‑based model to enhance performance. It evaluates existing end‑to‑end VO methods and explores their effectiveness on hand‑held camera data, contrasting with the typical driving‑scene training sets. The work aims to improve VO accuracy in more dynamic and complex visual environments.

By Ruiyu Li, Yinjia Liu, Alexander Yu

agents computer-vision robotics

arXiv AI

Jul 2

REALM: An RGB- and Event-Aligned Latent Manifold for Cross-Modal Perception

arXiv:2605. 00271v3 Announce Type: replace-cross Abstract: Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to extreme lighting.

By Vincenzo Polizzi, David B. Lindell, Jonathan Kelly

diffusion computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 15

An Attention-based Model for Robust Forecasting with Missing Modality

arXiv:2606. 13970v1 Announce Type: cross Abstract: Learning with missing modalities is a fundamental challenge in multimodal robot learning, as real-world robotic systems often operate in environments with incomplete sensor data.

By Zhitian Zhang, Wenjie Zi, Yunduz Rakhmangulova, Saghar Irandoust, Hossein Hajimirsadeghi, Thibaut Durand

llms robotics multimodal