We observe that MIM essentially teaches the model to learn better middle-level interactions among patches and extract more generalized features.
Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.
Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i. e., structure adaptation and distribution adaptation.
Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain.
In this work we develop a simple yet powerful framework, whose key idea is to select a subset of training examples from the unlabeled data when performing existing SSL methods so that only the unlabeled examples with pseudo labels related to the labeled data will be used to train models.
Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background.
In this paper, we first point out that current contrastive methods are prone to memorizing background/foreground texture and therefore have a limitation in localizing the foreground object.
This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.
Ranked #1 on Face Verification on IJB-C (training dataset metric)
In this paper, we repurpose the well-known Transformer and introduce a Face Transformer for supervised face clustering.
As a result, practical solutions on label assignment, scale-level data augmentation, and reducing false alarms are necessary for advancing face detectors.
The domain diversities including inconsistent annotation and varied image collection conditions inevitably exist among different facial expression recognition (FER) datasets, which pose an evident challenge for adapting the FER model trained on one dataset to another one.
The set of triplet constraints has to be sampled within the mini-batch.
Ranked #17 on Metric Learning on CUB-200-2011 (using extra training data)
Hence, we propose to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency.
Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction model.