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20212025
most citedTo Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis

20 citations · 102 across the 20 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024★ 12 cited

Open-Sora: Democratizing Efficient Video Production for All

Zangwei Zheng, Xiangyu Peng, Tianji Yang +6

Vision and language are the two foundational senses for humans, and they build up our cognitive ability and intelligence. While significant breakthroughs have been made in AI langu…

cs.CV2023★ 4 cited

Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models

Zangwei Zheng, Mingyuan Ma, Kai Wang +3

Continual learning (CL) can help pre-trained vision-language models efficiently adapt to new or under-trained data distributions without re-training. Nevertheless, during the conti…

cs.CV2023★ 7 cited

InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning

Ziheng Qin, Kai Wang, Zangwei Zheng +8

Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training. This could lead to…

cs.CV2022★ 16 cited

Prompt Vision Transformer for Domain Generalization

Zangwei Zheng, Xiangyu Yue, Kai Wang +1

Though vision transformers (ViTs) have exhibited impressive ability for representation learning, we empirically find that they cannot generalize well to unseen domains with previou…

cs.CV2021★ 2 cited

Multi-source Few-shot Domain Adaptation

Xiangyu Yue, Zangwei Zheng, Colorado Reed +3

Multi-source Domain Adaptation (MDA) aims to transfer predictive models from multiple, fully-labeled source domains to an unlabeled target domain. However, in many applications, re…

cs.CV2021

Scene-aware Learning Network for Radar Object Detection

Zangwei Zheng, Xiangyu Yue, Kurt Keutzer +1

Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in s…

cs.CV2021★ 15 cited

Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation

Xiangyu Yue, Zangwei Zheng, Shanghang Zhang +4

Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain. In some applications, however, it is expensive ev…