Unsupervised Domain Adaptive Object Detection (DAOD) task can relax the domain shift problem between source and target domains, which requires to train models on labeled source and unlabeled target domains jointly. However, due to limitations of data privacy protection, the source domain data is usually inaccessible, which poses significant challenges for the DAOD task. Hence, Source-Free Object Detection (SFOD) task has been developed that aims to fine-tune a pre-trained source model with only unlabeled target domain data. Most of the existing SFOD methods are based on pseudo labeling using the student-teacher framework, where the teacher model is the Exponential Moving Average (EMA) of the student models in different time steps. However, these methods always exist a knowledge bias problem due to class imbalance, and therefore, a fixed EMA update rate is no longer suitable for different classes. For high-quality classes, a fast EMA rate can accelerate knowledge updating and promote model convergence, while for low-quality classes, a fast EMA rate can accelerate the accumulation of knowledge bias and lead to the collapse of such categories. To solve this problem, we propose a novel SFOD method called Slow-Fast Adaptation which develops two different teacher models, a slow teacher, and a fast teacher model, to jointly guide the student training. The slow and fast teacher models can provide richer supervision information and complement each other. The experiments on four benchmark datasets show that our method achieves state-of-the-art results and even outperforms DAOD methods in some cases, which demonstrate the effectiveness of our method on the SFOD task.

Source-free object detection (SFOD) aims to adapt the source detector to unlabeled target domain data in the absence of source domain data. Most SFOD methods follow the same self-training paradigm using mean-teacher (MT) framework where the student model is guided by only one single teacher model. However, such paradigm can easily fall into a training instability problem that when the teacher model collapses uncontrollably due to the domain shift, the student model also suffers drastic performance degradation. To address this issue, we propose the Periodically Exchange Teacher-Student (PETS) method, a simple yet novel approach that introduces a multiple-teacher framework consisting of a static teacher, a dynamic teacher, and a student model. During the training phase, we periodically exchange the weights between the static teacher and the student model. Then, we update the dynamic teacher using the moving average of the student model that has already been exchanged by the static teacher. In this way, the dynamic teacher can integrate knowledge from past periods, effectively reducing error accumulation and enabling a more stable training process within the MT-based framework. Further, we develop a consensus mechanism to merge the predictions of two teacher models to provide higher-quality pseudo labels for student model. Extensive experiments on multiple SFOD benchmarks show that the proposed method achieves state-of-the-art performance compared with other related methods, demonstrating the effectiveness and superiority of our method on SFOD task.

Recently, there has been increasing interest in the Source-Free Domain Adaptive Object Detection task, which involves training an object detector on the unlabeled target data using a pre-trained source model without accessing the source data. Most related methods are developed from the mean-teacher framework, which aims to train the student model closer to the teacher model via a pseudo labeling manner, where the teacher model is the exponential-moving-average of the student models at different time-steps. Following this line of works, we propose a Run-and-Chase Mutual-Learning method to strengthen the interactions between the student model and the teacher model in both feature and prediction levels. In our method, the student model is optimized to run away from the teacher model at the feature level, while chasing the teacher model at the prediction level. In this way, the student model is forced to be distinguishable at different time-steps, so that the teacher model can acquire more diverse task-related information and produce higher-accuracy pseudo labels. As the training goes, the student and teacher models are updated iteratively and promoted mutually, which can prevent the model collapse problem. Extensive experiments are conducted to validate the effectiveness of our method.

Predicting individual aesthetic preferences holds significant practical applications and academic implications for human society. However, existing studies mainly focus on learning and predicting the commonality of facial attractiveness, with little attention given to Personalized Facial Beauty Prediction (PFBP). PFBP aims to develop a machine that can adapt to individual aesthetic preferences with only a few images rated by each user. In this paper, we formulate this task from a meta-learning perspective that each user corresponds to a meta-task. To address such PFBP task, we draw inspiration from the human aesthetic mechanism that visual aesthetics in society follows a Gaussian distribution, which motivates us to disentangle user preferences into a commonality and an individuality part. To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism. Unlike conventional meta-learning methods that may struggle with slow adaptation or overfitting to tiny support sets, we propose a novel approach that optimizes a high-order predictor for fast adaptation. In order to validate the performance of the proposed method, we build several PFBP benchmarks by using existing facial beauty prediction datasets rated by numerous users. Extensive experiments on these benchmarks demonstrate the effectiveness of the proposed MetaFBP method.