# Diversifying Joint Vision-Language Tokenization Learning Source: [Diversifying Joint Vision-Language Tokenization Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:27:18.619Z Authors: Vardaan Pahuja, AJ Piergiovanni, Anelia Angelova Published: 2023-06-06T05:41:42.000Z ## Abstract Building joint representations across images and text is an essential step for tasks such as Visual Question Answering and Video Question Answering. In this work, we find that the representations must not only jointly capture features from both modalities but should also be diverse for better generalization performance. To this end, we propose joint vision-language representation learning by diversifying the tokenization learning process, enabling tokens that are sufficiently disentangled from each other to be learned from both modalities. We observe that our approach outperforms the baseline models in a majority of settings and is competitive with state-of-the-art methods. ## 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]()