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cs.CV2024
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
Yongcheng Li, Lingcong Cai, Ying Lu +8
Accurate classification of blood cells is of vital significance in the diagnosis of hematological disorders. However, in real-world scenarios, domain shifts caused by the variabili…
cs.CV2024
Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model
Yongcheng Li, Lingcong Cai, Ying Lu +7
Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions. In this study, we present a nove…
cs.CL2024★ 2 cited
A Challenge Dataset and Effective Models for Conversational Stance Detection
Fuqiang Niu, Min Yang, Ang Li +3
Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discus…