arXiv:2604.15678v2 Announce Type: replace
Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) met...
By Eunju Lee, MiHyeon Kim, JuneHyoung Kwon, Yoonji Lee, JiHyun Kim, Soojin Jang, YoungBin Kim
The paper investigates why reducing the modality gap between image and text representations in CLIP does not always improve zero‑shot classification accuracy. It shows that while average alignment improves, the relative decision margins among classes can shift, leading to a prediction‑level hubness where predictions concentrate on a few classes. Experiments across datasets confirm that accuracy drops correlate with increased prediction concentration for both linear and learning‑based gap corrections.
By Shota Sato, Hajime Kiyama, Tosho Hirasawa, Mamoru Komachi
arXiv:2609.06967v1 Announce Type: cross
Abstract: Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many exist...
By Seungmin Oh, Seunghun Kang, Jongbin Ryu
arXiv:2602. 21397v2 Announce Type: replace-cross Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights.
By Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh, Ramtin Pedarsani
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 introduces UnInfo, a test‑time adaptation method for vision‑language models like CLIP that addresses image corruption—a realistic distribution shift caused by sensor conditions. UnInfo leverages uniformity‑aware confidence maximization, information‑aware loss balancing, and knowledge distillation from an EMA teacher to preserve embedding uniformity and improve zero‑shot classification accuracy. Experiments show that UnInfo outperforms existing TTA methods on corrupted image datasets.
By Kazuki Adachi, Shin'ya Yamaguchi, Tomoki Hamagami