BEiT: BERT Pre-Training of Image Transformers | OpenReview
Jan 28, 2022 · We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in …
We pretrain BEIT and conduct extensive fine-tuning experiments on downstream tasks, such as image classification, and semantic segmentation. We present that the self-attention …
如何评价微软提出的BEIT-3：通过多路Transformer实现多模态统一 …
Aug 23, 2022 · 总结BEIT-3和VLMO的对比，我的感叹是BEIT-3真的很简洁，少了很多trick，依然可以达到更好的效果。 在规模的提升面前，很多的trick变的无足轻重了。
如何看待BEIT V2？是否是比MAE更好的训练方式？ - 知乎
BEiT v2最核心的贡献是使用了VQ-KD作为视觉标志的生成结构，对比BEiT v1的dVAE，BEiT v2使用教师系统来引导视觉标志的生成，因为作为教师系统的CLIP或是DINO本身就是非常出色的 …
BEiT v2: Masked Image Modeling with Vector-Quantized Visual …
Feb 1, 2023 · Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most …
如何评价微软提出的无监督视觉模型BEiT：ImageNet达 …
为什么要构建视觉词表：直接使用像素级的自动编码器进行视觉预训练会促使模型关注短距离的依赖性和高频的细节（Ramesh等人，2021）。 BEIT通过预测离散的视觉词汇克服了上述问 …
Context Autoencoder for Self-Supervised Representation Learning
Feb 1, 2023 · Metareview: Summary, Strengths And Weaknesses: The paper proposes the Contextual Autoencoder (CAE) for self-supervised learning. It builds on MAE and BEiT to …
Corrupted Image Modeling for Self-Supervised Visual Pre-Training
Feb 1, 2023 · Abstract: We introduce Corrupted Image Modeling (CIM) for self-supervised visual pre-training. CIM uses an auxiliary generator with a small trainable BEiT to corrupt the input …
如何评价微软提出的BEIT-3：通过多路Transformer实现多模态统一 …
今天我们来聊一篇非常有意思的最新研究——来自Salesforce等机构的 BLIP3-o 模型。最近，像OpenAI的GPT-4o 这样的模型展示了强大的统一多模态能力，它们不仅能理解图像内容，还能 …
EVF-SAM: Early Vision-Language Fusion For Text-Prompted …
Sep 12, 2024 · Our experiments show that the proposed EVF-SAM based on BEIT-3 can obtain state-of-the-art performance on RefCOCO/+/g for referring expression segmentation and …