The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text pairs, enabling strong zero-shot classification and retrieval capabilities on various domains. However, CLIP's training remains computationally intensive, with high demands on both data processing and memory. To address these challenges, recent masking strategies have emerged, focusing on the selective removal of image patches to improve training efficiency. Although effective, these methods often compromise key semantic information, resulting in suboptimal alignment between visual features and text descriptions. In this work, we present a concise yet effective approach called Patch Generation-to-Selection to enhance CLIP's training efficiency while preserving critical semantic content. Our method introduces a gradual masking process in which a small set of candidate patches is first pre-selected as potential mask regions. Then, we apply Sobel edge detection across the entire image to generate an edge mask that prioritizes the retention of the primary object areas. Finally, similarity scores between the candidate mask patches and their neighboring patches are computed, with optimal transport normalization refining the selection process to ensure a balanced similarity matrix. Our approach, CLIP-PGS, sets new state-of-the-art results in zero-shot classification and retrieval tasks, achieving superior performance in robustness evaluation and language compositionality benchmarks.

翻译：CLIP模型通过在图像-文本对上进行大规模预训练，在视觉与语言模态对齐方面取得了显著进展，使其在多个领域展现出强大的零样本分类与检索能力。然而，CLIP的训练过程仍具有较高的计算复杂度，对数据处理和内存资源的需求较大。为应对这些挑战，近期出现了多种掩码策略，通过选择性移除图像补丁来提升训练效率。尽管这些方法有效，但往往损失关键语义信息，导致视觉特征与文本描述之间的对齐效果欠佳。本研究提出了一种简洁而有效的方法——补丁生成到选择，旨在提升CLIP训练效率的同时保留关键语义内容。该方法引入渐进式掩码过程：首先预选一小部分候选补丁作为潜在掩码区域；随后在整个图像上应用Sobel边缘检测生成边缘掩码，以优先保留主要目标区域；最后计算候选掩码补丁与其相邻补丁的相似度分数，并通过最优传输归一化优化选择过程，确保获得平衡的相似度矩阵。我们提出的CLIP-PGS方法在零样本分类与检索任务中取得了新的最先进成果，在鲁棒性评估和语言组合性基准测试中均表现出优越性能。