1 citations · 1 across the 4 of their papers we have counts for
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CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
Hualiang Wang, Yi Li, Huifeng Yao +1
Out-of-distribution (OOD) detection refers to training the model on an in-distribution (ID) dataset to classify whether the input images come from unknown classes. Considerable eff…
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
Zheang Huai, Xinpeng Ding, Yi Li +1
In the domain adaptation problem, source data may be unavailable to the target client side due to privacy or intellectual property issues. Source-free unsupervised domain adaptatio…
Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
Qixiang Zhang, Yi Li, Cheng Xue +1
Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and app…
A Closer Look at the Explainability of Contrastive Language-Image Pre-training
Yi Li, Hualiang Wang, Yiqun Duan +2
Contrastive language-image pre-training (CLIP) is a powerful vision-language model that has shown great benefits for various tasks. However, we have identified some issues with its…
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
Yi Li, Zhanghui Kuang, Liyang Liu +2
Most weakly supervised semantic segmentation (WSSS) methods follow the pipeline that generates pseudo-masks initially and trains the segmentation model with the pseudo-masks in ful…
Data-Driven Neuron Allocation for Scale Aggregation Networks
Yi Li, Zhanghui Kuang, Yimin Chen +1
Successful visual recognition networks benefit from aggregating information spanning from a wide range of scales. Previous research has investigated information fusion of connected…