With the rise of large-scale foundation models, efficiently adapting them to downstream tasks remains a central challenge. Linear probing, which freezes the backbone and trains a lightweight head, is ...

Time Vision Transformer. Contribute to ExplainableML/TiViT development by creating an account on GitHub.

Accurate global glacier mapping is critical for understanding climate change impacts. It is challenged by glacier diversity, difficult-to-classify debris and big data processing. Here we propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal global-scale glacier mapping using open satellite imagery. Assessing the spatial, temporal and cross-sensor generalisation shows that our best strategy achieves intersection over union >0.85 on previously unobserved images in most cases, which drops to >0.75 for debris-rich areas such as High-Mountain Asia and increases to >0.90 for regions dominated by clean ice. Additionally, adding synthetic aperture radar data, namely, backscatter and interferometric coherence, increases the accuracy in all regions where available. The calibrated confidence for glacier extents is reported making the predictions more reliable and interpretable. We also release a benchmark dataset that covers 9% of glaciers worldwide. Our results support efforts towards automated multitemporal and global glacier mapping.

This article describe about a paper of Genie in ICML 2024.

A deep learning model using open satellite data for scalable, global glacier mapping is developed. This model matches expert-level accuracy, facilitating more reliable glacier monitoring to support cl...

画像セットから視覚概念を推論する新手法「VICIS」を発表。

import torch import torch.nn as nn class VisionTransformer(nn.Module): def **init**(self, act_layer=None, **kwargs): super().**init**() # Default to GELU if none provided if act_layer is None: act_layer = nn.GELU # Support both nn.ReLU and nn.ReLU() styles self.act = act_layer() if isinstance(act_layer, type) else act_layer # Example MLP block using activation self.mlp = nn.Sequential( nn.Linear(768, 3072), self.act, nn.Linear(3072, 768) ) def forward(self, x): return self.mlp(x) # Example usage: if **name** == “**main** ”: model = VisionTransformer() x = torch.randn(1, 768) out = model(x) print(out.shape) Solution provided by Triskel Data Deterministic AI.