How Do Vision Transformers Work? ICLR 2022·Namuk Park,Songkuk Kim· The success of multi-head self-attentions (MSAs) for computer vision is now indisputable. However, little is known about how MSAs work. We present fundamental explanations to help better understand the nature of MSAs. In ...
[2202.06709] 论文题目:How Do Vision Transformers Work? 论文地址:http://arxiv.org/abs/2202.06709 代码:https://github.com/xxxnell/how-do-vits-work ICLR2022 - Reviewer Kvf7: 这个文章整理的太难懂了 很多trick很有用,但是作者并没有完全说明 行文线索 Emporocal Observations: MSAs(多头自注意力机制 /...
1.3 传统Transformer探究 2. 问题1:What properties of MSAs do we need to improve optimization? 3. 问题2: Do MSAs act like Convs? 4. 问题3: How can we harmonize MSAs with Convs? 5. AlterNet 6. 结论 2022-how-do-vits-work ICLR 论文题目:HOW DO VISION TRANSFORMERS WORK? 论文链接:https:...
ICLR 2022 摘要 用于计算机视觉的多头自注意力 (MSA) 的成功现在是无可争议的。然而,人们对 MSA 的工作原理知之甚少。我们提供基本解释,以帮助更好地理解 MSA 的性质。特别是,我们展示了 MSA 和 Vision Transformers (ViT) 的以下属性:1 MSA 不仅提高了准确性,还通过使损失情况变平来提高了泛化能力。这种改进...
How do Vision Transformers work? Vision Transformer model training Six applications of Vision Transformers A brief history of Transformers Attention mechanisms combined with RNNs were the predominant architecture for facing any task involving text until 2017, when a paper was published and changed every...
When vision transformers outperform resnets without pre- training or strong data augmentations. arXiv preprint arXiv:2106.01548, 2021. 3 [8] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In IEEE/CVF Conference on Computer ...
2 Background and Related Work In this paper, we explore a bug caused by the combined use of three popular components in computer vision: window attention, absolute position embeddings, and high resolution finetuning. Window Attention. In transformers, global attention (Vaswani et al., 2017) ...
Supervised Multimodal Bitransformers for Classifying Images and Text (Kiela et al. 2019) ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks (Lu et al. 2019) VL-BERT: Pretraining of Generic Visual-Linguistic Representations (Su et al. ICLR 2020) ...
Transformers for Video Generation. Transformer-based networks have shown promising and often superior perfor- mance not only in natural languages processing tasks [10, 43, 59], but also in computer vision related efforts [12, 20, 27, 41, 42]. Recent works prvoide promisin...
Little did they know that the paper would change the way we do machine learning. Ten years later, it even bagged the prestigious “Test of Time” award at the latest NeurIPS conference. To identify the most impactful paper in the past decade, the conference organisers selected a list of ...