PoE-Fuse: Precision-Weighted Expert Fusion for Bi-Temporal Change Understanding

arXiv:2609.37485 · cs.CV, cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/Neurogica/PoE-Fuse

Terminology

Alignment — 13× in this paper · explained in 1 episode
Ablation — 9× in this paper · explained in 1 episode
Fine-Tuning — 9× in this paper · explained in 5 episodes
Benchmark — 7× in this paper · explained in 2 episodes
Transformer — 6× in this paper · explained in 2 episodes
Adaptation — 4× in this paper · explained in 1 episode
Foundation Model — 4× in this paper · explained in 3 episodes
Grounding — 4× in this paper · explained in 3 episodes
Reinforcement Learning — 4× in this paper · explained in 4 episodes
Foundation Models — 3× in this paper · explained in 7 episodes
International Conference on Learning Representations — 3× in this paper
Vision-language models — 3× in this paper · explained in 2 episodes
Embeddings — 2× in this paper · explained in 2 episodes
Generative Model — 2× in this paper · explained in 2 episodes
Generative Models — 2× in this paper · explained in 3 episodes
Ablation study — 1× in this paper · explained in 1 episode
Accuracy — 1× in this paper · explained in 1 episode
Conference on Computer Vision and Pattern Recognition — 1× in this paper
Few-Shot Learning — 1× in this paper · explained in 4 episodes
IEEE International Conference on Image Processing — 1× in this paper
International Conference on Machine Learning — 1× in this paper
Multimodal Learning — 1× in this paper · explained in 2 episodes
Regularization — 1× in this paper · explained in 3 episodes
Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Terminology

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