title: Hard Patches Mining for Masked Image Modeling accepted: CVPR 2023 paper: https://arxiv.org/abs/2304.05919 code: https://github.com/Haochen-Wang409/HPM ref: CVPR 2023 | 挖掘困难样本的 MIM 框架: Hard Patches Mining for Masked Image Modeling 关键词:MIM, self-supervised, 自监督掩码学习...
Uni4Eye can serve as a global feature extractor, which builds its basis on a Masked Image Modeling task with a Vision Transformer (ViT) architecture. We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input ...
在第2 部分理论推导中,我们提到经过 k 层 GNN,输出的隐表示包含了 k 跳子图的聚合信息,这部分信息会存在 task irrelevant 的重叠与冗余,因此在掩码策略中,构建了两种掩码途径来减轻冗余。 Edge-wise random masking:使用伯努利分布得到掩码子集,再对原始边集进行随机掩码。
closely related research in vision [49, 39] preceded BERT.” 要特别赞一下这句话,其实也是有共鸣的,今年在RACV上讲了一个态度比较鲜明(或者极端吧。。)的talk,说要“重建CV人的文化自信”,就拿它作为其中一个例子:Mask Image Modeling或者视觉里叫Inpainting的方法在CV里做的蛮早的,在BERT之前就已经有一些...
Inspired by masked language modeling [6,22], masked image modeling (MIM) approaches are proposed for learning unsupervised image [36, 89] or video represen- tations [30, 71], which have been shown to be effective for many downstream tasks including image ...
Full size image Fig. 3: Generation trajectory of a molecule each for a 1% and a 5% masking rate. The model is trained on ChEMBL, and generation is carried out using training initialization. a 1% masking rate. b 5% masking rate. Full size image Figure 2a shows the trajectory of a molec...
We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image...
Second, MIM solves the image prediction task by training the encoder and decoder together , and does not design a separate task for the encoder . To further enhance the performance of the encoder when performing downstream tasks, we designed the encoder for the tasks of comparative learning and...
We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from ...
Recently, self-supervised Masked Autoencoders (MAE) have attracted unprecedented attention for their impressive representation learning ability. However, the pretext task, Masked Image Modeling (MIM), reconstructs the missing local patches, lacking the global understanding of the image. This paper extend...