The success of contrastive learning depends on the construction and utilization of high-quality positive pairs. However, current methods face critical limitations on two fronts: on the construction side, both handcrafted and generative augmentations often suffer from limited diversity and risk semantic corruption; on the learning side, the absence of a quality assessment mechanism leads to suboptimal supervision where all pairs are treated equally. To tackle these challenges, we propose GenView++, a unified framework that addresses both fronts by introducing two synergistic innovations. To improve pair construction, GenView++ introduces a multi-source adaptive view generation mechanism to synthesize diverse yet semantically coherent views by dynamically modulating generative parameters across image-conditioned, text-conditioned, and image-text-conditioned strategies. Second, a quality-driven contrastive learning mechanism assesses each pair's semantic alignment and diversity to dynamically reweight their training contribution, prioritizing high-quality pairs while suppressing redundant or misaligned pairs. Extensive experiments demonstrate the effectiveness of GenView++ across both vision and vision-language tasks. For vision representation learning, it improves MoCov2 by +2.5% on ImageNet linear classification. For vision-language learning, it raises the average zero-shot classification accuracy by +12.31% over CLIP and +5.31% over SLIP across ten datasets, and further improves Flickr30k text retrieval R@5 by +3.2%.

翻译：对比学习的成功依赖于高质量正样本对的构建与利用。然而，现有方法面临两个关键局限：在构建层面，无论是手工设计还是生成式增强方法，往往存在多样性受限和语义损坏风险；在学习层面，缺乏质量评估机制导致所有样本对被平等对待，产生次优监督。为应对这些挑战，我们提出GenView++，一个通过两项协同创新统一解决上述问题的框架。为改进样本对构建，GenView++引入多源自适应视图生成机制，通过动态调制图像条件、文本条件及图像-文本条件三种策略的生成参数，合成兼具多样性与语义一致性的视图。其次，质量驱动对比学习机制评估每个样本对的语义对齐度与多样性，动态重加权其训练贡献，优先高质量样本对并抑制冗余或失准样本对。大量实验验证了GenView++在视觉与视觉-语言任务中的有效性。在视觉表示学习中，其将MoCov2在ImageNet线性分类任务上的性能提升+2.5%。在视觉-语言学习中，其在十个数据集上的平均零样本分类准确率较CLIP提升+12.31%，较SLIP提升+5.31%，并将Flickr30k文本检索的R@5指标进一步提升+3.2%。