As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency.However, such power comes at a cost: it incurs a hugecomputation burden and heavy mem…
Inspired by the composition of the action, the pre-action and the result of action might be important parts of an action, we introduce bi-direction LSTM with hierarchical structure. Additionally, the separated spatial-temporal attention is employed into our method. Furthermore, we find that ...
We refer to this approach as Bi-level Routing Attention (BRA), as it contains a region-level routing step and a token-level atten- tion step. By using BRA as the core building block, we propose BiFormer, a general vision transformer backbone that can be used for...
irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bilevel routing attention,...
through the low-level PID controller. The observations are composed of the current joint positions of follower robots and the image feed from 4 cameras. Next, we train ACT to predict the sequence of future actions given the current observations. An action here corresponds to the target joint po...
(2005). Manual asymmetries in bimanual reaching: The influence of spatial compatibility and visuospatial attention. Brain and Cognition, 57(1), 102A 105.Neely K, Binsted G, Heath M (2005) Manual asymmetries in bimanual reaching: the influence of spatial compatibility and visuospatial attention....
Spatial-temporal attentionBilinear poolingLow-redundancyInternational Journal of Machine Learning and Cybernetics - With the progressive development of ubiquitous computing, wearable human activity recognition is playing an increasingly important role...
graph with spatial attentionAxillary lymph node(ALN)segmentation in ultrasound images is important for the diagnosis and treatment of breast cancer.Recently,deep learning methods for automatic medical image segmentation have improved significantly.However,two problems arise.(1)A unified model is often ...
irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bilevel routing attention,...
The bi-level routing attention operates in two stages: first, the feature map is projected through three fully connected layers to generate the query, key, and value matrices. The query and key matrices are then downsampled using average pooling at the regional level, and the correlation ...