vision transformer

Revealing the Dark Secrets of Masked Image Modeling CVPR 2023 Revisiting adapters with adversarial training ICLR 2023 Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models NeurIPS 2023 Revisiting Vision Transformer from the View of Path Ensemble ICCV 2023 RGB No More: Minimally-Decoded JPEG Vision Transformers CVPR 2023 RIFormer: Keep Your Vision Backbone Effective but Removing Token Mixer CVPR 2023 Robustifying Token Attention for Vision Transformers ICCV 2023 SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation ICCV 2023 Scalable Video Object Segmentation with Simplified Framework ICCV 2023 Scale-Aware Modulation Meet Transformer ICCV 2023 Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning ICCV 2023 Scaling Novel Object Detection With Weakly Supervised Detection Transformers WACV 2023 Scaling Vision Transformers to 22 Billion Parameters ICML 2023 Scattering Vision Transformer: Spectral Mixing Matters NeurIPS 2023 Scratching Visual Transformer's Back with Uniform Attention ICCV 2023 SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation ICLR 2023 SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation ICML 2023 Segment Anything in High Quality NeurIPS 2023 SeiT: Storage-Efficient Vision Training with Tokens Using 1% of Pixel Storage ICCV 2023 Self-Distillation for Further Pre-training of Transformers ICLR 2023 SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition CVPR 2023 Self-Supervised Learning From Images With a Joint-Embedding Predictive Architecture CVPR 2023 Self-supervision through Random Segments with Autoregressive Coding (RandSAC) ICLR 2023 Semantic-Aware Superpixel for Weakly Supervised Semantic Segmentation AAAI 2023 SG-Former: Self-guided Transformer with Evolving Token Reallocation ICCV 2023