Vision Transformer必读系列之图像分类综述:Attention-based 将门创投 【AAAI2022】ShiftVIT: When Shift Operation Meets Vision Transformer 论文:【AAAI2022】When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism 代码:https://github.com/microsoft/SPACH B站作者讲解视频...
作者的解决方案其实很简单,观察发现虽然同一个token在不同层的attention map差别小,但是同一层不同head之间差距还是很大,因此作者额外加入一个HxH大小的矩阵,来对attention map进行re-attention,利用其他head的attention map来对当前attention map进行增强。 反应在公式上的变化就是在与V相乘之前先与theta矩阵相乘,看起来...
Too Long; Didn't ReadUni-OVSeg is a breakthrough in open-vocabulary segmentation, reducing the need for labor-intensive annotations. It improves vision systems across sectors like medical imaging and autonomous vehicles, while addressing potential biases in AI data....
Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability...
Well, ViT showed clearly visible improvements up to 64 layers or so. Techniques such asre-attentioncould help transformers go deeper. The first inkling about the generic nature of transformers (that I experienced) actually did not come from ViT or vision but from the time-series transformer mode...
It uses a ResNet-101 backbone and includes the dilated C5 stage, benefiting from both the deeper network and the increased feature resolution. DETR significantly outperforms baselines on large objects, which is very likely enabled by the non-local computations allowed by the transformer. But ...
AI challenger: A large-scale dataset for going deeper in image understanding. arXiv preprint arXiv:1711.06475 (2017). Niu, Y. et al. Counterfactual VQA: A cause-effect look at language bias. In IEEE Conference on Computer Vision and Pattern Recognition, 12700–12710 (2021). Zhu, Y., ...
Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference Haoran You1,2,†,*, Yunyang Xiong2,∗, Xiaoliang Dai2, Bichen Wu2, Peizhao Zhang2, Haoqi Fan2, Peter Vajda2, Yingyan (Celi...
Block-Recurrent Transformer is a novel Transformer model that leverages the recurrence mechanism of LSTMs to achieve significant perplexity improvements in language modeling tasks over long-range sequences.
Speeding Up the Vision Transformer with BatchNorm Deep Learning How integrating Batch Normalization in an encoder-only Transformer architecture can lead to reduced training time… Anindya Dey, PhD August 6, 2024 28 min read The Math Behind Keras 3 Optimizers: Deep Understanding and Applicatio...