该检测器的优秀性能是在不同的目标检测器框架下观察到的,包括 Mask R-CNN、Cascade Mask R-CNN 以及它们的增强版本。 在COCO 数据集上的实验结果表明,一个使用无标签 ImageNet-1K 预训练、带有普通 ViT-Huge 主干的 ViTDet 检测器的 AP^box 可以...
具体包括四个阶段:第一个阶段,输入是 H\times W\times 3 的图像,首先4x4分块得到 \frac{W}{4}\times\frac{H}{4} 个patch(即token),每个patch 通过全连接层转化为 C_1 维向量,这样就得到了 transformer block 的输入。因为该模块的输入输出特征维度是相同的,因此第一阶段输出是 \frac{W}{4}\times...
Guy with beard and blond hair in c Father, parent with beard teaching little son to use tool screwdriver. Teamwork and assistance concept. Boy, child busy Great Stirrup Cay beach Masculinity concept. Man with beard, biker in leather jacket sitting on motor bike in darkness, black background....
In this version, the EfficientViT segment anything models are trained using the image embedding extracted by [SAM ViT-H](https://github.com/facebookresearch/segment-anything) as the target. The prompt encoder and mask decoder are the same as [SAM ViT-H](https://github.com/facebookresearch...
c_learning cache_replacement caltrain camp_zipnerf cann capsule_em caql cascaded_networks cate causal_label_bias cbertscore cell_embedder cell_mixer cfq cfq_pt_vs_sa charformer ciw_label_noise ckd class_balanced_distillation clay clip_as_rnn cluster_gcn clustering_normalized_cuts cmmd cnn_...
C. 分块循环表示可以执行有效的长序列建模。我们对每个本地块进行并行编码以提高计算速度,同时对全局块进行循环编码以节省 GPU 内存。 RetNet 与 Transformers RetNet 建议充分利用两个领域的优点,并展示我们如何才能实现这一目标。它使用 Transformer 的可并行训练范例,而不是 RNN 低效且缓慢的自回归步骤。然而,在推...
Retrieved September 30, 2024, from https://github.com/facebookresearch/xformers. Zhang, F., Zhu, X., Dai, H., Ye, M., & Zhu, C. (2020). Distribution-aware coordinate representation for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern ...
vit_base_patch32_384 83.35 96.84 88.2M 12.7G 384 1.0 bicubic google/baidu(3c2f) vit_base_patch16_224 84.58 97.30 86.4M 17.0G 224 0.875 bicubic google/baidu(qv4n) vit_base_patch16_384 85.99 98.00 86.4M 49.8G 384 1.0 bicubic google/baidu(wsum) vit_large_patch16_224 85.81 97.82 304.1...
SinCUT results (c), when trained on each input pair (a-b), demonstrate that it works well when transferring low-level information (top), but fails when higher level reasoning is required (bottom). (d) Our method suc- cessfully transfers the appearance across semantic regions...
hidden_states = model.vit.encoder.layer[l](hidden_states, layer_head_mask, output_attentions)[0] output = model.vit.layernorm(sequence_output) Pooler Generally, in a Transformer model Pooler is a component used to aggregate information from the sequence of tokens embeddings after the t...