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When a deep neural network is trained on data with only image-level labeling,the regions activated in each image tend to identify only a small region of thetarget object. We propose a method of using videos automatically harvested fromthe web to identify a larger region of the target object by using temporalinformation, which is not present in the static image. The temporal variationsin a video allow different regions of the target object to be activated. Weobtain an activated region in each frame of a video, and then aggregate theregions from successive frames into a single image, using a warping techniquebased on optical flow. The resulting localization maps cover more of the targetobject, and can then be used as proxy ground-truth to train a segmentationnetwork. This simple approach outperforms existing methods under the same levelof supervision, and even approaches relying on extra annotations. Based onVGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4,respectively, on PASCAL VOC 2012 test images, which represents a newstate-of-the-art.

Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation