Cat: cross attention in vision transformer. arXiv preprint arXiv:2106.05786v1 2022. Nguyen NQ, Jang G, Kim H, Kang J. Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction.
We use a single layer MLP as the lightweight predictor. We calculate the negative cosine similarity based on Eq. (5). Full size image Algorithm 1 Curriculum Mixup in SimSiam. Full size image Dataset Our experiments are conducted using the GI endoscopic dataset, HyperKvasir18. In total, there...
In this study, we introduce KPGT58, a self-supervised learning framework designed to enhance molecular representation learning, and thus advance the downstream molecular property prediction tasks. The KPGT framework combines a high-capacity model, called Line Graph Transformer (LiGhT), particularly desi...
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more - Transitioning default_cfg -> pretrained_cfg. Improving handling of pr
4.3 Transformer-Based Encoder Module As shown in Fig. 2, we compare our proposed encoder mod- ule with several common competitive methods. Multi-layer Perception (MLP) is a simple baseline architecture to extract point features. This method maps each point into different dimensions and extracts ...
To address these concerns, some recent studies have investigated the SegFormer architecture, which is a lightweight transformer-based architecture with hierarchically structured encoders whose multi-scale features are passed into a multi-layer perceptron (MLP)-based decoder [16]. We therefore hypothesize...
The automatic generation of realistic images directly from a story text is a very challenging problem, as it cannot be addressed using a single image gener
Cat: cross attention in vision transformer. arXiv preprint arXiv:2106.05786v1 2022. Quan et al. BMC Bioinformatics (2025) 26:57 Page 16 of 16 27. Nguyen NQ, Jang G, Kim H, Kang J. Perceiver CPI: a nested cross-attention network for compound-protein interac- tion ...
Transformer-Based Architectures for Medical Image Segmentation Drawing inspiration from the success of transformers in the field of CV, several scholars have endeavored to incorporate transformer components to improve the performance of medical image segmentation. Vision transformers (ViTs) [18] have ...
Self-supervised learning methods using the Transformer structure, such as Vision Transformer (ViT) [25], show excellent performance across various visual tasks. Additionally, the Swin-Transformer [26], optimized through hierarchical and sliding window strategies, further enhances the performance of the ...