Compositional Zero-Shot Learning (CZSL) aims to transfer knowledge from seen
state-object pairs to novel unseen pairs. In this process, visual bias caused
by the diverse interrelationship of state-object combinations blurs their
visual features, hindering the learning of distinguishable class prototypes.
Prevailing methods concentrate on disentangling states and objects directly
from visual features, disregarding potential enhancements that could arise from
a data viewpoint. Experimentally, we unveil the results caused by the above
problem closely approximate the long-tailed distribution. As a solution, we
transform CZSL into a proximate class imbalance problem. We mathematically
deduce the role of class prior within the long-tailed distribution in CZSL.
Building upon this insight, we incorporate visual bias caused by compositions
into the classifier's training and inference by estimating it as a proximate
class prior. This enhancement encourages the classifier to acquire more
discernible class prototypes for each composition, thereby achieving more
balanced predictions. Experimental results demonstrate that our approach
elevates the model's performance to the state-of-the-art level, without
introducing additional parameters. Our code is available at
https://github.com/LanchJL/ProLT-CZSL.

将组合零样本学习转化为相对类别不平衡问题，通过估计视觉偏差作为类别先验来训练分类器，以获取更可辨别的类原型，从而提高模型性能。

揭示组成式零样本学习中的近邻长尾分布

Revealing the Proximate Long-Tail Distribution in Compositional  Zero-Shot Learning

Deep neural networks are highly susceptible to learning biases in visual
data. While various methods have been proposed to mitigate such bias, the
majority require explicit knowledge of the biases present in the training data
in order to mitigate. We argue the relevance of exploring methods which are
completely ignorant of the presence of any bias, but are capable of identifying
and mitigating them. Furthermore, we propose using Bayesian neural networks
with an epistemic uncertainty-weighted loss function to dynamically identify
potential bias in individual training samples and to weight them during
training. We find a positive correlation between samples subject to bias and
higher epistemic uncertainties. Finally, we show the method has potential to
mitigate visual bias on a bias benchmark dataset and on a real-world face
detection problem, and we consider the merits and weaknesses of our approach.

提出一种基于贝叶斯神经网络和认知不确定性加权损失函数的方法，动态识别可能训练样本中的偏见并在训练期间对其加权，以减弱深度神经网络在视觉数据中出现的学习偏见。在偏见基准数据集和实际面部检测问题上，展示了这种方法缓解视觉偏见的潜力，并考虑了方法的优点和缺点。

视觉偏差缓解的知识不确定性加权损失

Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation

NLVR2 (Suhr et al., 2019) was designed to be robust for language bias through
a data collection process that resulted in each natural language sentence
appearing with both true and false labels. The process did not provide a
similar measure of control for visual bias. This technical report analyzes the
potential for visual bias in NLVR2. We show that some amount of visual bias
likely exists. Finally, we identify a subset of the test data that allows to
test for model performance in a way that is robust to such potential biases. We
show that the performance of existing models (Li et al., 2019; Tan and Bansal
2019) is relatively robust to this potential bias. We propose to add the
evaluation on this subset of the data to the NLVR2 evaluation protocol, and
update the official release to include it. A notebook including an
implementation of the code used to replicate this analysis is available at
this http URL

本技术报告分析了 NLVR2 的潜在视觉偏见，并提出通过测试可消除偏差来测试模型表现的数据子集。结果表明现有模型的表现相对稳健，因此建议将该子集数据纳入 NLVR2 的评估协议更新。