Zero-shot object detection (ZSD) aims to leverage semantic descriptions to localize and recognize objects of both seen and unseen classes. Existing ZSD works are mainly coarse-grained object detection, where the classes are visually quite different, thus are relatively easy to distinguish. However, in real life we often have to face fine-grained object detection scenarios, where the classes are too similar to be easily distinguished. For example, detecting different kinds of birds, fishes, and flowers. In this paper, we propose and solve a new problem called Fine-Grained Zero-Shot Object Detection (FG-ZSD for short), which aims to detect objects of different classes with minute differences in details under the ZSD paradigm. We develop an effective method called MSHC for the FG-ZSD task, which is based on an improved two-stage detector and employs a multi-level semantics-aware embedding alignment loss, ensuring tight coupling between the visual and semantic spaces. Considering that existing ZSD datasets are not suitable for the new FG-ZSD task, we build the first FG-ZSD benchmark dataset FGZSD-Birds, which contains 148,820 images falling into 36 orders, 140 families, 579 genera and 1432 species. Extensive experiments on FGZSD-Birds show that our method outperforms existing ZSD models.

The task of Video Anomaly Detection (VAD) is to find anomalies existing in given videos, which has been extensively studied. This paper addresses the task of Video Anomaly Prediction (VAP), which is to predict whether any anomaly will happen in streaming videos. With VAP, we can intervene or alert before anomalies really occur, thus preventing damage to public life and property. VAP is a more significant yet challenging task, which currently has only one work in the literature. To challenge this task, we first propose the concepts of predictable and unpredictable anomalies, and define the VAP task in video surveillance scenario. Based on this definition, we then construct the first VAP dataset, which consists of 618 frame-level annotated videos, including 300 normal videos and 318 anomaly videos. Next, we propose a Dual-channel Video Anomaly Prediction (DVAP) method based on feature enhancement and a new annotation scheme. Finally, We evaluate the proposed method and compare it with related works from classification and regression perspectives. Experimental results show that DVAP can predict anomalies one second earlier than they really occur and achieves the best accuracy.

Video anomaly detection is to automatically identify predefined anomalous contents (e.g. abnormal objects, behaviors and scenes) in videos. The performance of video anomaly detection can be effectively improved by making the model focus more on the anomalous objects in videos. However, such existing approaches usually rely on pre-trained models, which not only require additional auxiliary information but also face the challenge of anomaly diversity in the real world. In this paper, we propose a new video anomaly detection method based on spatio-temporal relationships among objects. Concretely, we use a fully convolutional encoder-decoder network with symmetric skip connections as the backbone network, which can effectively extract features from the object regions at different scales. In the encoding stage, an attention mechanism is used to enhance the model’s understanding of the spatio-temporal relationships among various types of objects in the video. In the decoding stage, a dynamic pattern generator is designed to memorize the inter-object spatio-temporal relationships, which thus enhances the reconstructions of normal samples while making the reconstructions of abnormal samples more difficult. We conduct extensive experiments on three widely used video anomaly detection datasets CUHK Avenue , ShanghaiTech Campus and UCSD Ped2 , and the experimental results show that our proposed method can significantly improve the performance, and achieves state-of-the-art overall performance (considering both effectiveness and efficiency). In particular, our method achieves a state-of-the-art AUC of 98.4% on the UCSD Ped2 dataset that consists of various anomalies in real-world scenarios.

Few-shot object detection (FSOD) is to detect objects with a few examples. However, existing FSOD methods do not consider hierarchical fine-grained category structures of objects that exist widely in real life. For example, animals are taxonomically classified into orders, families, genera and species etc. In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm. To this end, on the one hand, we build the first large-scale and high-quality Hi-FSOD benchmark dataset HiFSOD-Bird, which contains 176,350 wild-bird images falling to 1,432 categories. All the categories are organized into a 4-level taxonomy, consisting of 32 orders, 132 families, 572 genera and 1,432 species. On the other hand, we propose the first Hi-FSOD method HiCLPL, where a hierarchical contrastive learning approach is developed to constrain the feature space so that the feature distribution of objects is consistent with the hierarchical taxonomy and the model's generalization power is strengthened. Meanwhile, a probabilistic loss is designed to enable the child nodes to correct the classification errors of their parent nodes in the taxonomy. Extensive experiments on the benchmark dataset HiFSOD-Bird show that our method HiCLPL outperforms the existing FSOD methods.

BACKGROUND: Predicting protein complexes from protein-protein interaction (PPI) networks has been studied for decade. Various methods have been proposed to address some challenging issues of this problem, including overlapping clusters, high false positive/negative rates of PPI data and diverse complex structures. It is well known that most current methods can detect effectively only complexes of size ≥3, which account for only about half of the total existing complexes. Recently, a method was proposed specifically for finding small complexes (size = 2 and 3) from PPI networks. However, up to now there is no effective approach that can predict both small (size ≤ 3) and large (size >3) complexes from PPI networks. RESULTS: In this paper, we propose a novel method, called CPredictor2.0, that can detect both small and large complexes under a unified framework. Concretely, we first group proteins of similar functions. Then, the Markov clustering algorithm is employed to discover clusters in each group. Finally, we merge all discovered clusters that overlap with each other to a certain degree, and the merged clusters as well as the remaining clusters constitute the set of detected complexes. Extensive experiments have shown that the new method can more effectively predict both small and large complexes, in comparison with the state-of-the-art methods. CONCLUSIONS: The proposed method, CPredictor2.0, can be applied to accurately predict both small and large protein complexes.

BACKGROUND: Long non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting lncRNA-protein interactions (lncRPIs) is an important way to study the functions of lncRNA. Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs. RESULTS: In this paper, we propose to boost the prediction of lncRPIs by fusing multiple protein-protein similarity networks (PPSNs). Concretely, we first construct four PPSNs based on protein sequences, protein domains, protein GO terms and the STRING database respectively, then build a more informative PPSN by fusing these four constructed PPSNs. Finally, we predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. Our experimental results show that the new approach outperforms the existing methods. CONCLUSION: Fusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRPIs.

GML has become the de facto standard for electronic spatial data exchange among the applications of Web and distributed geographic information systems (GISs). As more and more geographical data is presented in GML, it is necessary to develop techniques for managing GML documents in databases. A possible solution is to store GML data into object-relational databases from which the users can retrieve the interested data. In this paper, we propose an approach to map GML schema to object-relational database schema by using GML schema graph, and algorithms for storing / querying valid GML documents into / from the relations generated by the corresponding object-relational schema. Spatial and non-spatial features embedded in GML document are stored in object-oriented relations, and the structures and constrains defined in GML schema are also well preserved. A prototype for GML documents storing and querying based on the proposed method is implemented on the basis of the Oracle/Spatial system. Preliminary experiment results shows that our method is feasible and efficient.