# Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples Source: [Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:38:02.139Z Authors: Philipp J. R\\"osch, Norbert Oswald, Michaela Geierhos, Jind\\v{r}ich Libovick\\'y Published: 2024-03-05T11:38:48.000Z ## Abstract Current multimodal models leveraging contrastive learning often face limitations in developing fine-grained conceptual understanding. This is due to random negative samples during pretraining, causing almost exclusively very dissimilar concepts to be compared in the loss function. Consequently, the models struggle with fine-grained semantic differences. To address this problem, we introduce a novel pretraining method incorporating synthetic hard negative text examples. The hard negatives permute terms corresponding to visual concepts, leading to a more fine-grained visual and textual concept alignment. Further, we introduce InpaintCOCO, a new challenging dataset for assessing the fine-grained alignment of colors, objects, and sizes in vision-language models. We created the dataset using generative inpainting from COCO images by changing the visual concepts so that the images no longer match their original captions. Our results show significant improvements in fine-grained concept understanding across a wide range of vision-language datasets, including our InpaintCOCO dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()