TY - JOUR
AU1 - Pei, Gensheng
AU2 - Chen, Tao
AU3 - Wang, Yujia
AU4 - Cai, Xinhao
AU5 - Shu, Xiangbo
AU6 - Zhou, Tianfei
AU7 - Yao, Yazhou
AB - Abstract: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.
TI - Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
JF - Computing Research Repository
DO - 10.48550/arxiv.2503.17080
DA - 2025-03-21
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/seeing-what-matters-empowering-clip-with-patch-generation-to-selection-0THLHBd8Lm
VL - 2025
IS - 2503
DP - DeepDyve
ER -