225 citations · 1.4k across the 72 of their papers we have counts for
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TextDiffuser: Diffusion Models as Text Painters
Jingye Chen, Yupan Huang, Tengchao Lv +3
Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue,…
A Unified View of Masked Image Modeling
Zhiliang Peng, Li Dong, Hangbo Bao +2
Masked image modeling has demonstrated great potential to eliminate the label-hungry problem of training large-scale vision Transformers, achieving impressive performance on variou…
Non-Contrastive Learning Meets Language-Image Pre-Training
Jinghao Zhou, Li Dong, Zhe Gan +2
Contrastive language-image pre-training (CLIP) serves as a de-facto standard to align images and texts. Nonetheless, the loose correlation between images and texts of web-crawled d…
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment
Haoyu Song, Li Dong, Wei-Nan Zhang +2
CLIP has shown a remarkable zero-shot capability on a wide range of vision tasks. Previously, CLIP is only regarded as a powerful visual encoder. However, after being pre-trained b…
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Xiujun Li, Xi Yin, Chunyuan Li +9
Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks. While existing methods simply concatena…
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Weijie Su, Xizhou Zhu, Yue Cao +4
We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Trans…