CONTRASTIVE LEARNING WITHOUT FALSE NEGATIVE 给定指定的伪标签,下一个目标是修改实例级对比学习的目标,即等式1中的Linst,以x处理检测到的假负样本。本文讨论了利用假负样本的两种策略。首先,假负样本消除方法从负样本样本集中排除假负样本: 其次,考虑到锚样本及其假负样本被分配了相同的伪标签,本文将假负样本视为...
false negative elimination and reuse. Specifically, in the training process, our method eliminates false negatives by clustering and comparing the semantic similarity. Next, we reuse those eliminated false negatives to reconstruct new positive pairs to boost contrastive learning performance. Our experiments...
Boosting contrastive self- supervised learning with false negative cancellation. In WACV, 2022. 2 [25] Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, and Ludwig Schmidt. Patching open-vocabulary ...
Bibtex @article{2022false, title={FALSE: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image}, author={Zhang, Zhaoyang and Wang, Xuying and Mei, Xiaoming and Tao, Chao and Li, Haifeng}, journal={IEEE Geoscience and Remote Sensing ...
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Specifically, we introduce multimodal contrastive learing and prototype-based clustering methods that allows us to avoid semantic-related samples in same batch being represent as negative, while considering multiple negatives. At the same time, we proposed action-guided-attention mechanism to capture core...
Moreover, the model provides various views by constructing novel positive and negative pairs in contrastive learning for gaining a better representation between different parts. The experimental results on the NYT10 dataset demonstrate our model surpasses the existing SOTA by more than 2.61% AUC score...
However, its effectiveness is seriously affected by the low-quality negative samples generated with random strategies. In this paper, we propose a Contrastive Perturbation Network (CPN), which introduces perturbation schemes into contrastive learning of weak supervised temporal sentence grounding. The ...
The contrastive learning task, which is constructed through unsupervised cross-domain image-to-image translation, provides a new insight for cross-domain transfer learning to overcome the problem of domain shift.Chen, RunfaTsinghua UniversitySun, Hanbing...
Then, driven by prior tissue knowledge, a multi-label retrieval-based contrastive learning module is proposed to effectively separate positive and negative imaging-report pairs by decreasing the disturbance made by hard-negative samples. In this way, the model can learn the essential and generalized ...