Class-wise Covariance Regularization is proposed, which aligns the predicted covariance structure of class confidences with the semantic correlations encoded in pretrained text embed-dings with the geometric consistency of the class space throughout fine-tuning, resulting in more stable and interpretable confidence distributions across categories.
PuRF is introduced, a novel PuRiFication-driven cache-based method for multi-label test-time adaptation of vision-language models that consistently outperforms state-of-the-art methods on ViT-B/32 across five datasets.
Yiwen Liang, Hui Chen, Yizhe Xiong et al.· 0 citations
TextFM is presented, the first language-guided feature matching framework that incorporates domain-invariant semantic information from vision-language models (VLMs) to establish a strong foundation for robust and generalizable feature matching under real-world constraints.
Zhihao Zheng, Jinglun Feng, Nirav Savaliya et al.· 2 citations
A unified CLIP-guided label-free region scoring framework for fine-grained classification is proposed, and random-crop-based pseudo-label scoring consistently outperforms SAM-based scoring across all datasets, suggesting that random crops preserve surrounding information and provide more stable semantic context when ps...
This work evaluates Discrepancy-Guided Parameter Suppression on the challenging iWildCam dataset under camera-induced domain shifts, providing the first in-depth assessment of DPS on a real-world distribution shift benchmark.
Chak Pang Hong· International Conference on...· 0 citations
The Robust and Fine-grained training framework for CLIP-based vision-language models (RoFLIP) is proposed, enhancing both the robustness and granularity of vision-language alignment and underscore RoFLIP’s compositional reasoning and generalization abilities.
Yiwei Sun, Chuan-Bin Liu, Shan-Cheng Fang et al.· International Journal of Com...· 0 citations
Experimental results on CIFAR-10, CIFAR-100, Animal-10N, and Mini-WebVision, together with additional evaluation under open-set noise, show that the proposed CANNE method achieves competitive performance across diverse noisy-label settings.
Ge Jin, Qian Zhang, Li Huang et al.· Entropy· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
