Comments (1)
- The performance difference comes from the fusion, current version is based on prediction level fusion for simplication, so the performance gain is slight (but it has) when reference image num changes from 2 to 5. For the feature level fusion, you need revise the model and test the related model.
- I will check the results. You can also download the results of COSNet from davis benchmark (https://davischallenge.org/davis2016/soa_compare.html).
from cosnet.
Related Issues (20)
- About sample_range parameter
- AttributeError: 'CoattentionModel' object has no attribute 'main_classifier' HOT 5
- Error after filling a val_seqs.txt HOT 1
- about the attention in CoattentionModel HOT 2
- 关于训练得到的co_attention.pth问题 HOT 2
- 测试代码问题 HOT 4
- 训练阶段创建的saliency_data.txt问题 HOT 4
- out of memory during training HOT 5
- why don't the test code correspondes to paper HOT 1
- Can you provide the source image and GT of The Youtube-Objects?
- 数据集文件夹结构
- CRF postprocessing code
- Could you upload pretrained weight again onto BaiduNetdisk?
- 测试输出异常
- A BIG final LOSS value HOT 1
- Using Group attention get worse result
- Segmentation result
- About FBMS evaluation
- Hi! Where is the pretrained model of your MATNet?
- where is saliency_data.txt
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from cosnet.