👥 Social Computing¶

🎞️ ECCV2024 · 2 paper notes

Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels: Proposes the DaSC framework, which simultaneously addresses the joint problem of long-tailed distribution and noisy labels through distribution-aware class centroid estimation (DaCC) and confidence-aware contrastive learning (SBCL + MIDL), achieving SOTA results on CIFAR and real-world noisy datasets.
GRACE: Graph-Based Contextual Debiasing for Fair Visual Question Answering: Proposes GRACE (GRAph-based Contextual DEbiasing), a graph-based contextual debiasing method. Through unsupervised context graph learning and graph-based diverse in-context example selection, it addresses the data bias inherited by large language models in knowledge-enhanced VQA systems.