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Better weakly supervised object detection by using absolutely wrong data with NDI-WSOD

Better weakly supervised object detection by using absolutely wrong data with NDI-WSOD


Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information



Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector.


... discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information.


... propose a negative deterministic information ... NDI-WSOD ... design several processes to identify and distill the NDI from negative instances online.


... utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively.


... method achieves satisfactory performance.



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