4 citations · 6 across the 6 of their papers we have counts for
6 papers
Frustratingly Easy Feature Reconstruction for Out-of-Distribution Detection
Yingsheng Wang, Shuo Lu, Jian Liang +2
Out-of-distribution (OOD) detection helps models identify data outside the training categories, crucial for security applications. While feature-based post-hoc methods address this…
Harmonizing and Merging Source Models for CLIP-based Domain Generalization
Yuhe Ding, Jian Liang, Bo Jiang +3
CLIP-based domain generalization aims to improve model generalization to unseen domains by leveraging the powerful zero-shot classification capabilities of CLIP and multiple source…
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks
Yuhe Ding, Bo Jiang, Aihua Zheng +2
Vision language models (VLMs) like CLIP show stellar zero-shot capability on classification benchmarks. However, selecting the VLM with the highest performance on the unlabeled dow…
Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances
Shuo Lu, Yingsheng Wang, Lijun Sheng +3
Out-of-distribution (OOD) detection aims to detect test samples outside the training category space, which is an essential component in building reliable machine learning systems.…
Which Model to Transfer? A Survey on Transferability Estimation
Yuhe Ding, Bo Jiang, Aijing Yu +2
Transfer learning methods endeavor to leverage relevant knowledge from existing source pre-trained models or datasets to solve downstream target tasks. With the increase in the sca…
Unleashing the power of Neural Collapse for Transferability Estimation
Yuhe Ding, Bo Jiang, Lijun Sheng +2
Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all. Prior studie…