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most citedExploring Training on Heterogeneous Data with Mixture of Low-rank Adapters

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cs.CV2024★ 1 cited

Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters

Yuhang Zhou, Zihua Zhao, Haolin Li +4

Training a unified model to take multiple targets into account is a trend towards artificial general intelligence. However, how to efficiently mitigate the training conflicts among…

cs.CV2024

Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning

Zihua Zhao, Mengxi Chen, Tianjie Dai +4

Noisy correspondence that refers to mismatches in cross-modal data pairs, is prevalent on human-annotated or web-crawled datasets. Prior approaches to leverage such data mainly con…