  With the recent burst of 2D and 3D data, cross-modal retrieval has attracted
increasing attention recently. However, manual labeling by non-experts will
inevitably introduce corrupted annotations given ambiguous 2D/3D content.
Though previous works have addressed this issue by designing a naive division
strategy with hand-crafted thresholds, their performance generally exhibits
great sensitivity to the threshold value. Besides, they fail to fully utilize
the valuable supervisory signals within each divided subset. To tackle this
problem, we propose a Divide-and-conquer 2D-3D cross-modal Alignment and
Correction framework (DAC), which comprises Multimodal Dynamic Division (MDD)
and Adaptive Alignment and Correction (AAC). Specifically, the former performs
accurate sample division by adaptive credibility modeling for each sample based
on the compensation information within multimodal loss distribution. Then in
AAC, samples in distinct subsets are exploited with different alignment
strategies to fully enhance the semantic compactness and meanwhile alleviate
over-fitting to noisy labels, where a self-correction strategy is introduced to
improve the quality of representation. Moreover. To evaluate the effectiveness
in real-world scenarios, we introduce a challenging noisy benchmark, namely
Objaverse-N200, which comprises 200k-level samples annotated with 1156
realistic noisy labels. Extensive experiments on both traditional and the newly
proposed benchmarks demonstrate the generality and superiority of our DAC,
where DAC outperforms state-of-the-art models by a large margin. (i.e., with
+5.9% gain on ModelNet40 and +5.8% on Objaverse-N200).
