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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing
Published:
- 论文链接：https://arxiv.org/abs/2308.14036
- 代码链接：https://github.com/FVL2020/ICCV-2023-MB-TaylorFormer
- 会议： ICCV(2023)
- Keywords： Transformer, Image Dehazing
A survey on federated learning
Published:
- 论文链接：https://www.sciencedirect.com/science/article/pii/S0950705121000381?via%3Dihub
- 会议/期刊： Knowledge-based systems （CCFC SCI一区）
- Keywords：Federaled learning, Privacy protection
Understanding Robustness of Transformers for Image Classification
Published:
- 论文链接：https://arxiv.org/abs/2103.14586
- 会议： ICCV(2021)
- Keywords： CNN, Robustness, VIT, ResNet
Deep Image Prior
Published:
- 论文链接：https://arxiv.org/abs/1711.10925
- 会议： International Journal of Computer Vision （2020）
- Keywords： CNN, generative deep networks, inverse problems, image restoration
Adding Conditional Control to Text-to-Image Diffusion Models
Published:
- 论文链接：https://arxiv.org/abs/2302.05543
- 会议： ICCV23 Best Paper
- Keywords： Deeplearning, Diffusion Model
portfolio
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publications
Efficient Prompt Tuning for Vision and Language Models
Published in 20 November, 2023
Abstract: Recently, large-scale pre-trained visual language models have demonstrated excellent performance in many downstream tasks. A more efficient adapta-tion method for different downstream tasks is prompt tuning, which fixes the parameters of the visual language model and adjusts only prompt pa-rameters in the process of adapting the downstream tasks, using the knowledge learned by the visual language model during pre-training to solve the problems in the down-stream tasks. However, the loss of the downstream task and the original loss of the visual language model are not exactly same during model training. For example, CLIP uses contrast learn-ing loss to train the model, while the downstream image classification task uses the cross-entropy loss commonly used in classification problems. Dif-ferent loss has different guiding effects on the task. The trend of the accura-cy of the visual language model task during training is also different from that with the downstream task. The choice of an appropriate loss function and a reasonable prompt tuning method have a great impact on the perfor-mance of the model. Therefore, we pro-pose a more efficient method of prompt tuning for CLIP, experiments on 11 datasets demonstrate that our method achieves better performance and faster convergence in the down-stream task.
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talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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