most citedOut-of-Distribution Detection: A Task-Oriented Survey of Recent Advances

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

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…

cs.LG2024★ 4 cited

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.…

cs.LG2024★ 2 cited

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…