2 citations · 4 across the 5 of their papers we have counts for
4 papers
Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances
Zhendong Chu, Ruiyi Zhang, Tong Yu +4
To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumul…
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +4
Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the…
AIMS: All-Inclusive Multi-Level Segmentation
Lu Qi, Jason Kuen, Weidong Guo +5
Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for different-level region-of-…
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation
Li Xu, Haoxuan Qu, Jason Kuen +2
Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video. However, d…