42 citations · 56 across the 9 of their papers we have counts for
10 papers
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…
FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation
Yi Li, Huifeng Yao, Hualiang Wang +1
Fully supervised semantic segmentation learns from dense masks, which requires heavy annotation cost for closed set. In this paper, we use natural language as supervision without a…
Exploring Visual Interpretability for Contrastive Language-Image Pre-training
Yi Li, Hualiang Wang, Yiqun Duan +2
Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily available supervision of natural language. It improves the performance of downstream vision t…
Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images
Yi Li, Yiduo Yu, Yiwen Zou +2
Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis; however, the high cost of pixel-level annotation…
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
Yi Li, Yiqun Duan, Zhanghui Kuang +3
Weakly-Supervised Semantic Segmentation (WSSS) segments objects without a heavy burden of dense annotation. While as a price, generated pseudo-masks exist obvious noisy pixels, whi…
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…