Considering the close connection between action recognition and human pose estimation, we design a Collaboratively Self-supervised Video Representation (CSVR) learning framework specific to action recognition by jointly factoring in generative pose prediction and discriminative context matching as pretext tasks. Specifically, our CSVR consists of three branches: a generative pose prediction branch, a discriminative context matching branch, and a video generating branch. Among them, the first one encodes dynamic motion feature by utilizing Conditional-GAN to predict the human poses of future frames, and the second branch extracts static context features by contrasting positive and negative video feature and I-frame feature pairs. The third branch is designed to generate both current and future video frames, for the purpose of collaboratively improving dynamic motion features and static context features. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple popular video datasets.

Domain generalization (DG) attempts to learn a model on source domains that can well generalize to unseen but different domains. The multiple source domains are innately different in distribution but intrinsically related to each other, e.g., from the same label space. To achieve a generalizable feature, most existing methods attempt to reduce the domain discrepancy by either learning domain-invariant feature, or additionally mining domain-specific feature. In the space of these features, the multiple source domains are either tightly aligned or not aligned at all, which both cannot fully take the advantage of complementary information from multiple domains. In order to preserve more complementary information from multiple domains at the meantime of reducing their domain gap, we propose that the multiple domains should not be tightly aligned but composite together, where all domains are pulled closer but still preserve their individuality respectively. This is achieved by using instance-adaptive classifier specified for each instance’s classification, where the instance-adaptive classifier is slightly deviated from a universal classifier shared by samples from all domains. This adaptive classifier deviation allows all instances from the same category but different domains to be dispersed around the class center rather than squeezed tightly, leading to better generalization for unseen domain samples. In result, the multiple domains are harmoniously composite centered on a universal core, like a dandelion, so this work is referred to as DandelionNet. Experiments on multiple DG benchmarks demonstrate that the proposed method can learn a model with better generalization and experiments on source free domain adaption also indicate the versatility.

Recently, self-supervised video representation learning based on Masked Video Modeling (MVM) has demonstrated promising results for action recognition. However, existing methods face two significant challenges: (1) video actions involve a crucial temporal dimension, yet current masking strategies adopt inefficient random approaches that undermine low-density dynamic motion clues in videos; (2) pre-training requires large-scale datasets and significant computing resources (including large batch sizes and enormous iterations). To address these issues, we propose a novel method named Data-Efficient Masked Video Modeling (DEMVM) for self-supervised action recognition. Specifically, a novel masking strategy named Flow-Guided Dense Masking (FGDM) is proposed to facilitate efficient learning by focusing more on the action-related temporal clues, which applies dense masking to dynamic regions based on optical flow priors, while sparse masking to background regions. Furthermore, DEMVM introduces a 3D video tokenizer to enhance the modeling of temporal clues. Finally, Progressive Masking Ratio (PMR) and 2D initialization strategies are presented to enable the model to adapt to the characteristics of the MVM paradigm during different training stages. Extensive experiments on multiple benchmarks, UCF101, HMDB51, and Mimetics, demonstrate that our method achieves state-of-the-art performance in the downstream action recognition task with both efficient data and low computational cost. More interestingly, the few-shot experiment on the Mimetics dataset shows that DEMVM can accurately recognize actions even in the presence of context bias.

Domain adaptation attempts to boost the performance on a target domain by borrowing knowledge from a well established source domain. To handle the distribution gap between two domains, the prominent approaches endeavor to extract domain-invariant features. It is known that after a perfect domain alignment the domain-invariant representations of two domains should share the same characteristics from perspective of the overview and also any local piece. Inspired by this, we propose a novel method called Hierarchical Gradient Synchronization to model the synchronization relationship among the local distribution pieces and global distribution, aiming for more precise domain-invariant features. Specifically, the hierarchical domain alignments including class-wise alignment, group-wise alignment and global alignment are first constructed. Then, these three types of alignment are constrained to be consistent to ensure better structure preservation. As a result, the obtained features are domain invariant and intrinsically structure preserved. As evaluated on extensive domain adaptation tasks, our proposed method achieves state-of-the-art classification performance on both vanilla unsupervised domain adaptation and partial domain adaptation.

Domain adaptation attempts to transfer the knowledge obtained from the source domain to the target domain, i.e., the domain where the testing data are. The main challenge lies in the distribution discrepancy between source and target domain. Most existing works endeavor to learn domain invariant representation usually by minimizing a distribution distance, e.g., MMD and the discriminator in the recently proposed generative adversarial network (GAN). Following the similar idea of GAN, this work proposes a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation. Specifically, our proposed network consists of three parts, an encoder, a generator and two discriminators. The encoder embeds samples from both domains into the latent representation, and the generator decodes the latent representation to both source and target domains respectively conditioned on a domain code, i.e., achieves domain transformation. The generator is pitted against duplex discriminators, one for source domain and the other for target, to ensure the reality of domain transformation, the latent representation domain invariant and the category information of it preserved as well. Our proposed work achieves the state-of-the-art performance on unsupervised domain adaptation of digit classification and object recognition.

Face recognition is an important problem in computer vision, however, it is still challenging due to a few wild factors, such as large variations caused by pose, expression, lighting, etc. In this work, we mainly focus on dealing with the pose variations for face recognition. The proposed method attempts to directly transform a non-frontal face image into frontal one by Learning a Displacement Field network (LDFNet) and then recognizes with the transformed images. The existing methods, that follow the same scheme of transforming non-frontal faces into frontal ones, either transform by using 3D-model (3D methods) or transform by using 2D reconstructive methods (2D methods). The 3D methods may lead to the invisibility of some pixels in the transformed frontal images, while the 2D methods may lead to difference between the pixels in the transformed frontal images and the original non-frontal images. Our proposed LDF-Net method can handle these two problems by learning a morphable displacement field for each pixel in the transformed frontal image. Therefore, LDF-Net can achieve a frontal image where all pixels are from the original non-frontal image pixels and no invisible pixels exist, so as to maintain the informative information from the non-frontal images as much as possible. The experiments on MultiPIE dataset show that the proposed LDF-Net achieves state-of-theart performance for face recognition across pose, especially for those large poses.