4 accepted papers
Causal inference has emerged as a promising approach for identifying decisive semantic factors and eliminating spurious correlations in visual representation learning. However, most existing methods rely on latent, data-driven confounder modeling, normally attributing the source of bias to backgroun
Incorporating tagging information to regularize the representation learning of images usually leads to improved performance in image classification by aligning the visual features with the textual ones of higher discriminative power. Existing methods typically follow the predictive approach, which u…
Causal inference has emerged as a promising approach to mitigate long-tail classification by handling the biases introduced by class imbalance. However, along with the change of advanced backbone models from Convolutional Neural Networks (CNNs) to Visual Transformers (ViT), existing causal models ma
Cross-modal alignment is an effective approach to improving visual classification. Existing studies typically enforce a one-step mapping that uses deep neural networks to project the visual features to mimic the distribution of textual features. However, they typically face difficulties in finding s