2 citations · 6 across the 7 of their papers we have counts for
7 papers
Asking like Socrates: Socrates helps VLMs understand remote sensing images
Run Shao, Ziyu Li, Zhaoyang Zhang +9
Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseud…
BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF
Kaiwen Duan, Hongwei Yao, Yufei Chen +4
Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning text-to-image (T2I) models with human preferences. However, RLHF's feedback mechanism also opens new pathw…
Generalized Categories Discovery for Long-tailed Recognition
Ziyun Li, Christoph Meinel, Haojin Yang
Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set…
ImbaGCD: Imbalanced Generalized Category Discovery
Ziyun Li, Ben Dai, Furkan Simsek +2
Generalized class discovery (GCD) aims to infer known and unknown categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising known classes. Existing…
Supervised Knowledge May Hurt Novel Class Discovery Performance
Ziyun Li, Jona Otholt, Ben Dai +3
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given tha…
A Closer Look at Novel Class Discovery from the Labeled Set
Ziyun Li, Jona Otholt, Ben Dai +3
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing res…
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge
Ziyun Li, Xinshao Wang, Di Hu +4
Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under advers…