68 citations · 216 across the 24 of their papers we have counts for
7 papers · 1 filter
Minimal Semantic Sufficiency Meets Unsupervised Domain Generalization
Tan Pan, Kaiyu Guo, Dongli Xu +8
The generalization ability of deep learning has been extensively studied in supervised settings, yet it remains less explored in unsupervised scenarios. Recently, the Unsupervised…
ALSA: Anchors in Logit Space for Out-of-Distribution Accuracy Estimation
Chenzhi Liu, Mahsa Baktashmotlagh, Yanran Tang +2
Estimating model accuracy on unseen, unlabeled datasets is crucial for real-world machine learning applications, especially under distribution shifts that can degrade performance.…
Improving Out-of-Distribution Detection via Dynamic Covariance Calibration
Kaiyu Guo, Zijian Wang, Tan Pan +2
Out-of-Distribution (OOD) detection is essential for the trustworthiness of AI systems. Methods using prior information (i.e., subspace-based methods) have shown effective performa…
GIQ: Benchmarking 3D Geometric Reasoning of Vision Foundation Models with Simulated and Real Polyhedra
Mateusz Michalkiewicz, Anekha Sokhal, Tadeusz Michalkiewicz +4
Modern monocular 3D reconstruction methods and vision-language models (VLMs) demonstrate impressive results on standard benchmarks, yet recent works cast doubt on their true unders…
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Kaiyu Guo, Tan Pan, Chen Jiang +5
Medical anomaly detection (AD) is crucial for early clinical intervention, yet it faces challenges due to limited access to high-quality medical imaging data, caused by privacy con…
Benchmarking Transferability: A Framework for Fair and Robust Evaluation
Alireza Kazemi, Helia Rezvani, Mahsa Baktashmotlagh
Transferability scores aim to quantify how well a model trained on one domain generalizes to a target domain. Despite numerous methods proposed for measuring transferability, their…
Feature Space Perturbation: A Panacea to Enhanced Transferability Estimation
Prafful Kumar Khoba, Zijian Wang, Chetan Arora +1
Leveraging a transferability estimation metric facilitates the non-trivial challenge of selecting the optimal model for the downstream task from a pool of pre-trained models. Most…