The proposed texture transformer consists of a learnable texture extractor which learns a jointly feature embedding for further attention computation and two attention based modules which transfer HR textures from the Ref image
Since our framework treats depth estimation as an auxiliary for visual odometry without special optimization, the improvement indicates that accurate camera pose estimation improves depth estimation in the proposed framework
Transformer in Image Quality takes the advantage of inductive capability of convolution neural networks architecture for quality feature derivation and Transformer encoder for aggregated representation of attention mechanism
We can see that our model SEgmentation TRansformer-PUP is superior to fully convolutional network baselines, and FCN plus attention based approaches, such as Non-local and CCNet; and its performance is on par with the best results reported so far
TransPose models match the state-of-the-art on COCO Keypoint Detection task that has been dominated by deep fully convolutional architectures, and there seems to have further space to improve the upper limit of model performance by expanding the size of TransPose
For Data-efficient image Transformers we have only optimized the existing data augmentation and regularization strategies pre-existing for convnets, not introducing any significant architectural beyond our novel distillation token
This paper introduces Pointformer, a highly effective feature learning backbone for 3D point clouds that is permutation invariant to points in the input and learns local and global context-aware representations
We introduced Vision Transformer-Faster R-CNN, a competitive object detection solution which utilizes a transformer backbone, suggesting that there are sufficiently different architectures from the well-studied CNN backbone plausible to make progress on complex vision tasks
Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computa...
Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing metho...