However, the majority of work on self-training has focused on the objective of improving accuracy whereas practical machine learning systems can havecomplex goals (e.g.

Neural Information Processing Systems http://nips.cc/

Inthiswork,wepropose anovelCrossAttention Network to address the challenging problems in few-shot classification.

Thesebiased models suffer from the poor generalization capability when evaluated on unbiased datasets. Existing approaches for debiasing often identify and emphasize those samples withnosuchcorrelation (i.e.,bias-conflicting)without defining the bias type in advance. However, such bias-conflicting samples are significantly scarce in biased datasets, limiting the debiasing capability of these approaches.

While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored.