# Enhancing Features in Long-tailed Data Using Large Vision Model Source: [Enhancing Features in Long-tailed Data Using Large Vision Model]() Collection index: [llms.txt]() Data updated: 2026-02-10T05:00:04.897Z Authors: Pengxiao Han, Changkun Ye, Jinguang Tong, Cuicui Jiang, Jie Hong, Li Fang, Xuesong Li Published: 2025-04-15T04:21:50.000Z ## Abstract Language-based foundation models, such as large language models (LLMs) or large vision-language models (LVLMs), have been widely studied in long-tailed recognition. However, the need for linguistic data is not applicable to all practical tasks. In this study, we aim to explore using large vision models (LVMs) or visual foundation models (VFMs) to enhance long-tailed data features without any language information. Specifically, we extract features from the LVM and fuse them with features in the baseline network's map and latent space to obtain the augmented features. Moreover, we design several prototype-based losses in the latent space to further exploit the potential of the augmented features. In the experimental section, we validate our approach on two benchmark datasets: ImageNet-LT and iNaturalist2018. ## 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]()