Some recent work [22, 66, 68] has discussed estimating probability maps of ego-attention or predicting gaze points in egocentric videos. However, people think not in terms of points in their field of view, but in terms of theobjects that they are attending to. Of course, the object of interest could be obtained by first estimating the gaze with the gaze estimator and generating object candidates from an off-theshelf object detector, and then picking the object that the estimated gaze falls in. Because this bottom-up approach estimateswhere and what separately, it could be doomed to fail if the eye gaze prediction is slightly inaccurate, such as falling between two objects or in the intersection ofmultiple object bounding boxes (Figure1).

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The first termkxt x αk2 can be simply merged in eq. The first term is consensus error and will be merged inT1 in eq. Response: We have conducted more experiments over the ImageNet37 dataset which is known as a complicated dataset. As the middle figure38 demonstrates, for a binary classification, our method significantly im-39 proves upon the benchmarks over this dataset as well. We also carried40 out experiments on a deeper neural network with 4 hidden layers and41 ourmethod providessignificant speedups overthebenchmarks (bottom42 figure).

For the generator and the three discriminators, we use the FFHQ [2] and AAHQ datasets with 1024 1024 resolution. Hence, cooperating withGAN inversion methods, our framework is able to achieve arbitrary style transfer of a given face image. Wheni=0,allthelayersofthegenerator areinfluenced bythestylelatentcode. Result images of the directly concatenating method have similar face identities and head poses to their reference images, which means that this method leaks content information ofreference images to stylelatentcodes. However, for a reference image whose style is significantly different from that inAAHQ, ifdirectly feeding itinto BlendGAN, the style ofgenerated images maynotbesimilartothereference.