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Author: KaihuaTang

lvis1.0/README.md

@@ -18,16 +18,18 @@ The hyper-parameters of the proposed TDE are actually in convfc_bbox_head.py rat
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 PORT=112222 bash ./tools/dist_test.sh configs/htc/htc_r101_fpn_20e_causal.py work_dirs/htc_r101_fpn_20e_causal/latest.pth 8 --eval bbox segm
 ```
 
-### Background Fixed TDE
-This is a very useful trick to apply TDE on object detection or instance segmentation. As we discussed in our supplementary materials, In object detection and instance segmentation tasks, the existing frameworks usually need a background category for the classifier to filter the trivial regions, which is also a head class. However, the preference towards the background is not just a good bias but also an essential bias. Otherwise, the results will be full of meaningless trivial bounding boxes or regions. Therefore, we propose a background-fixed TDE that freezes the foreground / background probability ratio before and after the TDE for each prediction. The background-fixed TDE can be formulated as:
+### Background-Exempted Inference
+This is a very useful trick to apply TDE on object detection or instance segmentation. As we discussed in our supplementary materials, In object detection and instance segmentation tasks, the existing frameworks usually need a background category for the classifier to filter the trivial regions, which is also a head class. However, the preference towards the background is not just a good bias but also an essential bias. Otherwise, the results will be full of meaningless trivial bounding boxes or regions. Therefore, we propose a Background-Exempted Inference that freezes the foreground / background probability ratio before and after the TDE for each prediction. The Background-Exempted Inference can be formulated as:
 
 ![alt text](bg-fix.png "from 'Supplementary Material of Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect'")
 
-The results with and without background-fixed TDE are reported as follows:
+where i = 0 is the background class, p_i = P(Y =i | do(X = x)) is the original probability, and q_i is the softmax activated probability of the TDE(Y_i).
+
+The results with and without Background-Exempted Inference are reported as follows:
 
 ![alt text](bg-fix-table.png "from 'Supplementary Material of Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect'")
 
-As we can see, the background-fixed strategy successfully prevents the TDE from hurting the foreground-background selection. It is the key to apply TDE in tasks like object detection and instance segmentation that include one or more legitimately biased head categories, i.e., this strategy allows us to conduct TDE on a selected subset of categories.
+As we can see, the Background-Exempted Inference strategy successfully prevents the TDE from hurting the foreground-background selection. It is the key to apply TDE in tasks like object detection and instance segmentation that include one or more legitimately biased head categories, i.e., this strategy allows us to conduct TDE on a selected subset of categories.
 
 ### Results