TecoPrompt introduces a closed‑loop robust prompt‑learning framework that mitigates label noise in vision‑language models. It uses optimal transport in the CLIP semantic space to generate globally consistent pseudo‑labels, then verifies their reliability by checking trajectory stability over a K‑epoch window and an EMA‑based confidence gate. The verified labels are incorporated into prompt training via a tri‑group objective, yielding significant accuracy gains across multiple noisy datasets, such as a 0.843 accuracy on OxfordPets with 50% asymmetric noise.
By Zeyi Shao, Haowen Hua, Jiaxin Zhang, John See, Zeyd Boukhers, Cong Yang
arXiv:2604. 06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks.
By Yaqi Zhao, Haoliang Sun, Yating Wang, Yongshun Gong, Yilong Yin
Prompt learning modifies vision‑language models by optimizing continuous prompt vectors, yet the resulting prompts are hard to interpret in natural language. PromptSpLiCE is a post‑hoc method that rewrites each class‑conditioned text embedding as a sparse mix of concepts from a fixed dictionary, enabling a direct comparison of concept profiles before and after prompt learning. Across 11 image‑classification datasets, the method shows that only about 1.6 of the initial top‑10 concepts remain after learning, and that larger profile changes correlate with higher accuracy gains, while a derived gradient expression offers geometric insight into loss sensitivity.
By Ryo Kamiya, Hiroshi Kera, Kazuhiko Kawamoto
arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.
By Seokhee Jin, Changhwan Sung, Sunung Mun, Hoyoung Kim, Jungseul Ok
The paper introduces EvoPrompt, a framework for adapting vision‑language models to new tasks with limited data while preventing catastrophic forgetting. EvoPrompt uses a Modality‑Shared Prompt Projector to create hierarchical prompts and an evolutionary training strategy that separates low‑rank updates into directional and magnitude components, preserving learned semantic directions. Experiments show that EvoPrompt achieves state‑of‑the‑art few‑shot performance while maintaining the original zero‑shot capabilities of the pre‑trained models.
By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li
ProCAP introduces a probabilistic cross-attentive prompt learning framework for vision-language models like CLIP, enabling improved cross-modal interaction without updating the backbone. It jointly learns visual and textual prompt tokens, linking them via stacked bidirectional multi-head cross-attention to refine each branch across prompt depth. The method incorporates Gaussian parameterization of prompt tokens, lightweight KL and L2 regularization, and a compact symmetric InfoNCE head to align image features with class-level text representations, achieving strong few-shot base-to-novel performance and competitive transfer results across multiple datasets and benchmarks.
By Hiwa Azeez Abbas, Fatemeh Daneshfar, Moloud Abdar
arXiv:2603. 09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge.
By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li
arXiv:2606. 06943v1 Announce Type: cross Abstract: Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but remain highly fragile under adversarial perturbations.
By Sunoh Kim, Daeho Um
The paper introduces ADAPT, an adversarial disentangled prompt tuning framework designed to improve the robustness of vision‑language models. ADAPT employs a dual‑prompt strategy: a target prompt learns robust features while a set of decoy prompts capture pseudo‑robust, non‑generalizable shortcuts. By enforcing orthogonality between target and decoy prompts, the method mitigates robust generalization overfitting and provides a theoretical error bound for unseen classes, leading to significant empirical robustness gains.
By Yang Chen, Zhan Zhuang, Yanbin Wei, Zebin Chen, Hua Liu, Yu Zhang
arXiv:2607. 03900v1 Announce Type: cross Abstract: Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.
By Siru Jiang, Jian Liang, Ran He, Tieniu Tan
arXiv:2608.29395v1 Announce Type: new
Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
By Pedram MohajerAnsari, Amir Salarpour, Run Wang, Mert D. Pes\'e
The paper investigates how different training strategies affect the prompt sensitivity of large language models. It reproduces and compares methods such as refined data construction and robustness objectives, finding that while robustness fine‑tuning improves over standard fine‑tuning and in‑context learning, the prompt gap remains large (40–57%). Notably, newer techniques like CoIN and PPCL often underperform a simple data‑construction approach that uses one template per batch, and diagnostics suggest that mixed‑template batches force the optimizer to reconcile conflicting updates rather than learn a prompt‑agnostic representation.
By Frederic Sadrieh, Michal \v{S}tef\'anik