7 papers
Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing Label Bias in Foundation Models
Beier Zhu, Kaihua Tang, Qianru Sun +1
Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised o…
Identifying Hard Noise in Long-Tailed Sample Distribution
Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua +2
Conventional de-noising methods rely on the assumption that all samples are independent and identically distributed, so the resultant classifier, though disturbed by noise, can sti…
Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization
Jiaxin Qi, Kaihua Tang, Qianru Sun +2
Out-Of-Distribution generalization (OOD) is all about learning invariance against environmental changes. If the context in every class is evenly distributed, OOD would be trivial b…
Invariant Feature Learning for Generalized Long-Tailed Classification
Kaihua Tang, Mingyuan Tao, Jiaxin Qi +2
Existing long-tailed classification (LT) methods only focus on tackling the class-wise imbalance that head classes have more samples than tail classes, but overlook the attribute-w…
Debiased Fine-Tuning for Vision-language Models by Prompt Regularization
Beier Zhu, Yulei Niu, Saeil Lee +2
We present a new paradigm for fine-tuning large-scale visionlanguage pre-trained models on downstream task, dubbed Prompt Regularization (ProReg). Different from traditional fine-t…
Prompt-aligned Gradient for Prompt Tuning
Beier Zhu, Yulei Niu, Yucheng Han +2
Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we can craft a zero-shot classifier by "prompt", e.g., the confidence score of an image being "[CLASS]" can…