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

arXiv:2605.00271 · cs.CV, cs.AI, cs.RO · Submitted 2026-04-30 · Read on arXiv

cs.CV, cs.AI, cs.RO

Submitted: 2026-04-30

Updated: 2026-09-18

Comments: In Proceedings of the European Conference on Computer Vision (ECCV), Malmö, SE, 2026

Journal ref: Computer Vision - ECCV 2026, Lecture Notes in Computer Science, vol. 17005, pp. 1-20, Springer, Cham (2026)

DOI: 10.1007/978-3-032-37369-4_1

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Distillation — 25× in this paper · explained in 2 episodes
Latent Space — 24× in this paper · explained in 7 episodes
Alignment — 22× in this paper · explained in 1 episode
AUC — 19× in this paper · explained in 2 episodes
Transformer — 16× in this paper · explained in 2 episodes
Foundation Model — 15× in this paper · explained in 3 episodes
Robustness — 12× in this paper · explained in 5 episodes
Adaptation — 9× in this paper · explained in 1 episode
Benchmark — 8× in this paper · explained in 2 episodes
Foundation Models — 8× in this paper · explained in 7 episodes
Downstream Tasks — 7× in this paper · explained in 2 episodes
Accuracy — 6× in this paper · explained in 1 episode
Fine-Tuning — 6× in this paper · explained in 5 episodes
Generalization — 6× in this paper · explained in 13 episodes
Modality Gap — 6× in this paper · explained in 4 episodes
Consistency — 5× in this paper · explained in 3 episodes
Low-Rank Adaptation (LoRA) — 5× in this paper · explained in 9 episodes
Sparsity — 5× in this paper · explained in 4 episodes
Embeddings — 4× in this paper · explained in 2 episodes
Feature Extraction — 4× in this paper · explained in 2 episodes
LoRA Adapters — 4× in this paper · explained in 3 episodes
Loss Function — 4× in this paper · explained in 2 episodes
Angular Error — 3× in this paper · explained in 1 episode
Composite Loss Function — 3× in this paper · explained in 2 episodes
Hallucination — 3× in this paper · explained in 3 episodes
Optical Flow — 3× in this paper · explained in 2 episodes
Ablation — 2× in this paper · explained in 1 episode
Ablation study — 2× in this paper · explained in 1 episode
AdamW Optimizer — 2× in this paper · explained in 1 episode
Area Under Curve — 2× in this paper
Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
Core — 2× in this paper · explained in 2 episodes
Multimodal Dataset — 2× in this paper · explained in 2 episodes
Temporal consistency — 2× in this paper · explained in 2 episodes
Aggregation — 1× in this paper · explained in 1 episode
Benchmarking — 1× in this paper · explained in 3 episodes
Calibration — 1× in this paper · explained in 7 episodes
Computational overhead — 1× in this paper · explained in 2 episodes
Cosine Similarity — 1× in this paper · explained in 3 episodes
Domain Shift — 1× in this paper · explained in 4 episodes
European Conference on Computer Vision — 1× in this paper
Grounding — 1× in this paper · explained in 3 episodes
Hybrid Approach — 1× in this paper · explained in 4 episodes
Knowledge Distillation — 1× in this paper · explained in 11 episodes
Multi-Scale Gradient — 1× in this paper
Point Clouds — 1× in this paper · explained in 2 episodes
Temporal Correlations — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
Verification — 1× in this paper · explained in 2 episodes
Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes
alpha — 1× in this paper · explained in 1 episode

Sources

DINOv3

Terminology

Sources

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