57 citations · 80 across the 10 of their papers we have counts for
14 papers
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Kunbo Ding, Weijie Liu, Yuejian Fang +3
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.…
Fully Self-Supervised Learning for Semantic Segmentation
Yuan Wang, Wei Zhuo, Yucong Li +3
In this work, we present a fully self-supervised framework for semantic segmentation(FS^4). A fully bootstrapped strategy for semantic segmentation, which saves efforts for the hug…
Semantic Matching from Different Perspectives
Weijie Liu, Tao Zhu, Weiquan Mao +4
In this paper, we pay attention to the issue which is usually overlooked, i.e., \textit{similarity should be determined from different perspectives}. To explore this issue, we rele…
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation
Ke Zhang, Sihong Chen, Qi Ju +3
With the increase in the number of image data and the lack of corresponding labels, weakly supervised learning has drawn a lot of attention recently in computer vision tasks, espec…
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders
Chen Xu, Bojie Hu, Yanyang Li +5
Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Autom…
Energy Aligning for Biased Models
Bowen Zhao, Chen Chen, Qi Ju +1
Training on class-imbalanced data usually results in biased models that tend to predict samples into the majority classes, which is a common and notorious problem. From the perspec…