activity
20202023
most citedAn Empirical Study of Remote Sensing Pretraining

229 citations · 669 across the 26 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CV2023★ 7 cited

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

Wenjie Xuan, Shanshan Zhao, Yu Yao +5

Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately…

cs.CV2023★ 2 cited

Multi-Granularity Hand Action Detection

Ting Zhe, Jing Zhang, Yongqian Li +3

Detecting hand actions in videos is crucial for understanding video content and has diverse real-world applications. Existing approaches often focus on whole-body actions or coarse…

cs.CV2023★ 2 cited

Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective

Yingying Fan, Yu Wu, Bo Du +1

We focus on the weakly-supervised audio-visual video parsing task (AVVP), which aims to identify and locate all the events in audio/visual modalities. Previous works only concentra…

cs.CL2023

Self-Evolution Learning for Discriminative Language Model Pretraining

Qihuang Zhong, Liang Ding, Juhua Liu +2

Masked language modeling, widely used in discriminative language model (e.g., BERT) pretraining, commonly adopts a random masking strategy. However, random masking does not conside…

cs.CL2023

Revisiting Token Dropping Strategy in Efficient BERT Pretraining

Qihuang Zhong, Liang Ding, Juhua Liu +4

Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at s…

cs.CV2023★ 6 cited

Scalable Mask Annotation for Video Text Spotting

Haibin He, Jing Zhang, Mengyang Xu +3

Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive vide…

cs.CL2023★ 148 cited

Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

Qihuang Zhong, Liang Ding, Juhua Liu +2

Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. Several prior studies have shown that ChatGPT attains rema…