Grid-Attention: Enhancing Computational Efficiency of Large Vision Models Without Fine-Tuning
No confident code implementation found for this paper. 1 possible match can be shown below.
No confident code implementation found for this paper. 1 possible match can be shown below.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs
Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections
Easing Training Process of Rectified Flow Models Via Lengthening Inter-Path Distance
ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations
Towards Robust Full Low-Bit Quantization of Super Resolution Networks
R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
@article{li2026gridattention,
title = {Grid-Attention: Enhancing Computational Efficiency of Large Vision Models Without Fine-Tuning},
author = {Pengyu Li and Tianchu Guo and Biao Wang and Xian-Sheng Hua},
year = {2026},
doi = {10.1007/978-3-031-72973-7_4},
url = {https://doi.org/10.1007/978-3-031-72973-7_4},
journal = {ECCV 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/261d19798d64cf2430307e30abd0135ae30d2c06)