arXiv AI July 2, 2026 By Vincenzo Polizzi, David B. Lindell, Jonathan Kelly

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.

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 AI.

arXiv Computer Vision

4d ago

SFE-VGGT: Source-Free VGGT Distillation for Event-Based Monocular Depth Estimation

arXiv:2609.36929v1 Announce Type: new Abstract: Recent event-based depth estimation methods successfully transfer geometric priors from vision foundation models via cross-modal distillation. However,...

By Thai Duy Nguyen, Addison Lin Wang

computer-vision efficiency multimodal

arXiv AI

Jun 30

CMTFormer: Marrying Transformer with Hierarchical Information Interaction for RGB-Event Object Detection

arXiv:2606. 29136v1 Announce Type: cross Abstract: Event cameras capture sparse brightness changes with high temporal resolution and high dynamic range, compensating for the deficiencies of the conventional RGB frames.

By Yu Li, Yuenan Hou, Yingmei Wei, Jiangming Chen, Yanming Guo

llms computer-vision multimodal benchmarks safety

arXiv Computer Vision

Sep 16

Hyper-RED: Scalable Event Pre-training via Semantic Hypergraph Distillation

Hyper-RED introduces a scalable image-to-event pretraining framework that transfers high‑order semantic structures via hypergraphs, avoiding rigid pixel‑wise alignment. By constructing image, event, and cross‑modal hypergraphs and applying a hypergraph relational distillation loss, the method preserves local relational consistency and event‑specific characteristics while inheriting image‑derived semantic organization. Experiments across five event datasets show consistent scaling from ViT‑S to ViT‑L and state‑of‑the‑art performance.

By Meisen Wang, Zhiqiang Tian, Wei Bao, Chengjie Wang, Shaoyi Du, Siqi Li

efficiency multimodal benchmarks safety

arXiv Computer Vision

Sep 23

LiFR v2: Completion-Augmented Event Propagation for High-Rate Dense Prediction

arXiv:2609.25803v1 Announce Type: new Abstract: High-rate dense perception in dynamic environments is limited by the low update rate of RGB cameras, as rapid scene changes can occur between frames. E...

By Tao Wan, Xiaoshan Wu, Yifei Yu, Bo Wang, Xiaoyang Lyu, Muxin Liu, Aoxuan Pan, Zhongrui Wang, Xiaojuan Qi

computer-vision

arXiv AI

Jul 2

Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning

arXiv:2607. 00514v1 Announce Type: cross Abstract: Automatic understanding of dynamic 4D point clouds, the 3D-point sequences captured over time by depth sensors and LiDAR, is central to robotics and embodied perception.

By Trung Thanh Nguyen, Hai Nguyen-Truong, Tu Vo, Hoang M. Truong, Tuan-Anh Vu

rag diffusion robotics fine-tuning efficiency multimodal benchmarks

arXiv AI

Jul 13

Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms

arXiv:2607. 09114v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.

By Peipei Zhu, Yueqing Niu, Lin Zhu, Guanchong Niu, Yang Yu, Zheng Li

rag multimodal benchmarks

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Related stories

SFE-VGGT: Source-Free VGGT Distillation for Event-Based Monocular Depth Estimation

CMTFormer: Marrying Transformer with Hierarchical Information Interaction for RGB-Event Object Detection

Hyper-RED: Scalable Event Pre-training via Semantic Hypergraph Distillation

LiFR v2: Completion-Augmented Event Propagation for High-Rate Dense Prediction

Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning

Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms