Search Results for author: Yanli Ji

Found 6 papers, 1 papers with code

Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

no code implementations24 Oct 2023 Qing Miao, Xiaohe Wu, Chao Xu, Yanli Ji, WangMeng Zuo, Yiwen Guo, Zhaopeng Meng

By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training.

Learning with noisy labels

Partial Feature Selection and Alignment for Multi-Source Domain Adaptation

no code implementations CVPR 2021 Yangye Fu, Ming Zhang, Xing Xu, Zuo Cao, Chao Ma, Yanli Ji, Kai Zuo, Huimin Lu

By assuming that the source and target domains share consistent key feature representations and identical label space, existing studies on MSDA typically utilize the entire union set of features from both the source and target domains to obtain the feature map and align the map for each category and domain.

feature selection Partial Domain Adaptation

Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos

no code implementations CVPR 2021 Mingxing Zhang, Yang Yang, Xinghan Chen, Yanli Ji, Xing Xu, Jingjing Li, Heng Tao Shen

Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation.

Sentence

Universal Weighting Metric Learning for Cross-Modal Matching

1 code implementation CVPR 2020 Jiwei Wei, Xing Xu, Yang Yang, Yanli Ji, Zheng Wang, Heng Tao Shen

Furthermore, we introduce a new polynomial loss under the universal weighting framework, which defines a weight function for the positive and negative informative pairs respectively.

Image-text matching Metric Learning +1

Learning to Optimize Non-Rigid Tracking

no code implementations CVPR 2020 Yang Li, Aljaž Božič, Tianwei Zhang, Yanli Ji, Tatsuya Harada, Matthias Nießner

One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear system in the inner loop.

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