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Found 6 papers, 5 papers with code
Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
This paper aims to bridge this gap by conducting a comprehensive review and analysis of object detectors in open environments.
Less is More: Fewer Interpretable Region via Submodular Subset Selection
For incorrectly predicted samples, our method achieves gains of 81. 0% and 18. 4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively.
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning
A multi-view contrastive learning framework is introduced to encompass semantic contrasts between source, codeswitched, and target sentences, as well as contrasts among token-to-token relations.
Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations
Different from existing models, in this paper, we propose a new interpretation method that explains the image similarity models by salience maps and attribute words.
Online estimating weight of white Pekin duck carcass by computer vision
The model estimated the weight of duck carcasses precisely with a mean abstract deviation (MAD) of 58. 8 grams and a mean relative error (MRE) of 2. 15% in the testing dataset.
Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR, and we further applied the SHapley Additive exPlanations (SHAP) framework to identify informative features and their interpretations.
