3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models
Yushuo Chen, Tianyi Tang, Erge Xiang +5
In real world, large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide…
cs.LG2024★ 1 cited
Transferring Core Knowledge via Learngenes
Fu Feng, Jing Wang, Xin Geng
The pre-training paradigm fine-tunes the models trained on large-scale datasets to downstream tasks with enhanced performance. It transfers all knowledge to downstream tasks withou…
cs.CV2024★ 3 cited
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
Jing Wang, Jinagyun Li, Chen Chen +3
The Few-Shot Segmentation (FSS) aims to accomplish the novel class segmentation task with a few annotated images. Current FSS research based on meta-learning focus on designing a c…