uCAP: An Unsupervised Prompting Method for Vision-Language Models
No confident code implementation found for this paper. 3 possible matches can be shown below.
No confident code implementation found for this paper. 3 possible matches can be shown below.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
UltraTWD: Optimizing Ultrametric Trees for Tree-Wasserstein Distance
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
PTTA: Purifying Malicious Samples for Test-Time Model Adaptation
Sharingan: A Transformer Architecture for Multi-Person Gaze Following
CLIFF: Continual Latent Diffusion for Open-Vocabulary Object Detection
BlockGCN: Redefine Topology Awareness for Skeleton-Based Action Recognition
@article{nguyen2026ucap,
title = {uCAP: An Unsupervised Prompting Method for Vision-Language Models},
author = {A. T. Nguyen and Kai Sheng Tai and Bor-Chun Chen and Satya Narayan Shukla and Hanchao Yu and Philip H. S. Torr and Tai-Peng Tian and Ser-Nam Lim},
year = {2026},
doi = {10.1007/978-3-031-72904-1_25},
url = {https://doi.org/10.1007/978-3-031-72904-1_25},
journal = {ECCV 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/3cefbfc1720908febe12714b008ac10915b12fe0)