Given a query text, the similarity is determined bythemost similar prototype tofind correspondence inthevideo, which is termed text-adaptive matching.

We show that if the predictor is accurate, we can efficiently bypass these impossibility results and achieve a constant-factor approximation to the optimal solution, i.e., consistency.

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Similarly to previous methods, our approach is fully unsupervised in a sense that it does not require or make any use of annotated landmarks for the target object category.

We have derived three sample-to-sample based losses in the manuscript, i.e., USS loss, sample-to-sample based softmax, and BCE losses. The experimental evaluations of such marginal losses have been included in Sec. In our work, we choose the cosine function to represent the similarity of two features, i.e., g (x, x The learning rate starts at 0.1 and is reduced by a factor of 10 at the All models in ablation and parameter study were trained on CASIA-WebFace. For Glint360K, we train the models(ResNet-100) for 20 epochs using a batch size of 1024. The UniTSFace under the'Large' protocol of MegaFace Challenge 1 (as shown in Table 4) was trained on Glint360K.

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ObjectNet is the same size as the ImageNet test set (50,000 images), and by design does not comepaired withatraining setinordertoencourage generalization.