ODN: Opening the Deep Network for Open-set Action Recognition

In recent years, the performance of action recognition has been significantly improved with the help of deep neural networks. Most of the existing action recognition works hold the \textit{closed-set} assumption that all action categories are known beforehand while deep networks can be well trained for these categories. However, action recognition in the real world is essentially an \textit{open-set} problem, namely, it is impossible to know all action categories beforehand and consequently infeasible to prepare sufficient training samples for those emerging categories. In this case, applying closed-set recognition methods will definitely lead to unseen-category errors. To address this challenge, we propose the Open Deep Network (ODN) for the open-set action recognition task. Technologically, ODN detects new categories by applying a multi-class triplet thresholding method, and then dynamically reconstructs the classification layer and "opens" the deep network by adding predictors for new categories continually. In order to transfer the learned knowledge to the new category, two novel methods, Emphasis Initialization and Allometry Training, are adopted to initialize and incrementally train the new predictor so that only few samples are needed to fine-tune the model. Extensive experiments show that ODN can effectively detect and recognize new categories with little human intervention, thus applicable to the open-set action recognition tasks in the real world. Moreover, ODN can even achieve comparable performance to some closed-set methods.Comment: 6 pages, 3 figures, ICME 201

Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation

Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with sufficient samples, which focuses on both base-class performance and novel-class generalization. A well known modification to the base-class training is to apply a margin to the base-class classification. However, a dilemma exists that we can hardly achieve both good base-class performance and novel-class generalization simultaneously by applying the margin during the base-class training, which is still under explored. In this paper, we study the cause of such dilemma for FSCIL. We first interpret this dilemma as a class-level overfitting (CO) problem from the aspect of pattern learning, and then find its cause lies in the easily-satisfied constraint of learning margin-based patterns. Based on the analysis, we propose a novel margin-based FSCIL method to mitigate the CO problem by providing the pattern learning process with extra constraint from the margin-based patterns themselves. Extensive experiments on CIFAR100, Caltech-USCD Birds-200-2011 (CUB200), and miniImageNet demonstrate that the proposed method effectively mitigates the CO problem and achieves state-of-the-art performance

Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection

Person Re-identification (Re-ID) has attracted great attention due to its promising real-world applications. However, in practice, it is always costly to annotate the training data to train a Re-ID model, and it still remains challenging to reduce the annotation cost while maintaining the performance for the Re-ID task. To solve this problem, we propose the Annotation Efficient Person Re-Identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation. Specifically, we design an annotation and training framework to firstly reduce the size of the alternative pair set by clustering all images considering the locality of features, secondly select images pairs from intra-/inter-cluster samples for human to annotate, thirdly re-assign clusters according to the annotation, and finally train the model with the re-assigned clusters. During the pair selection, we seek for valuable pairs according to pairs' fallibility and diversity, which includes an intra-cluster criterion to construct image pairs with the most chaotic samples and the representative samples within clusters, an inter-cluster criterion to construct image pairs between clusters based on the second-order Wasserstein distance, and a diversity criterion for clusterbased pair selection. Combining all criteria above, a greedy strategy is developed to solve the pair selection problem. Finally, the above clustering-selecting-annotating-reassigning-training procedure will be repeated until the annotation budget is reached. Extensive experiments on three widely adopted Re-ID datasets show that we can greatly reduce the annotation cost while achieving better performance compared with state-of-the-art works

Regulating Blood Clot Fibrin Films to Manipulate Biomaterial-Mediated Foreign Body Responses

The clinical efficacy of implanted biomaterials is often compromised by host immune recognition and subsequent foreign body responses (FBRs). During the implantation, biomaterials inevitably come into direct contact with the blood, absorbing blood protein and forming blood clot. Many studies have been carried out to regulate protein adsorption, thus manipulating FBR. However, the role of clot surface fibrin films formed by clotting shrinkage in host reactions and FBR is often ignored. Because of the principle of fibrin film formation being relevant to fibrinogen or clotting factor absorption, it is feasible to manipulate the fibrin film formation via tuning the absorption of fibrinogen and clotting factor. As biological hydroxyapatite reserved bone architecture and microporous structure, the smaller particle size may expose more microporous structures and adsorb more fibrinogen or clotting factor. Therefore, we set up 3 sizes (small, <0.2 mm; medium, 1 to 2 mm; large, 3 to 4 mm) of biological hydroxyapatite (porcine bone-derived hydroxyapatite) with different microporous structures to investigate the absorption of blood protein, the formation of clot surface fibrin films, and the subsequent FBR. We found that small group adsorbed more clotting factors because of more microporous structures and formed the thinnest and sparsest fibrin films. These thinnest and sparsest fibrin films increased inflammation and profibrosis of macrophages through a potential signaling pathway of cell adhesion–cytoskeleton–autophagy, leading to the stronger FBR. Large group adsorbed lesser clotting factors, forming the thickest and densest fibrin films, easing inflammation and profibrosis of macrophages, and finally mitigating FBR. Thus, this study deepens the understanding of the role of fibrin films in host recognition and FBR and demonstrates the feasibility of a strategy to regulate FBR by modulating fibrin films via tuning the absorption of blood proteins