Counterfactual Debiasing for Fact Verification - OpenReview 016 namely CLEVER, which is augmentation-free 017 and mitigates biases on the inference stage 018 Specifically, we train a claim-evidence fusion 019 model and a claim-only model independently 020 Then, we obtain the final prediction via sub-021 tracting output of the claim-only model from 022 output of the claim-evidence fusion model,
Weakly-Supervised Affordance Grounding Guided by Part-Level. . . In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric object images without dense labels
Probabilistic Learning to Defer: Handling Missing Expert. . . Recent progress in machine learning research is gradually shifting its focus towards *human-AI cooperation* due to the advantages of exploiting the reliability of human experts and the efficiency of AI models