Realistic human grasping data is of vital importance in the research of human grasping behavior analysis and human-like robotic grasping. A grasping dataset should include object shape information， contact points， and hand shapes and poses. However， related works often capture images or videos to estimate the human grasping behavior， which leads to the inaccuracy of joint degrees of freedom. Virtual Reality （VR） technology was used to establish a virtual environment， and digital gloves were used to directly capture 3D objects and hand poses in the virtual environment as capturing data. The proposed dataset contains 91 objects with various shapes （each with 108 poses） from 49 object categories， and 52 173 3D hand grasps， which scale and richness are far more than existing dataset used to study human grasping behavior and human-centered grasp technology. In addition， the collected dataset was used for grasp saliency analysis and human-like grasping calculation， and the experimental results demonstrate the practical value of this dataset.

The outlier detection algorithm based on autoencoder is easy to over-fit on small- and medium-sized datasets， and the traditional outlier detection algorithm based on ensemble learning does not optimize and select the base detectors， resulting in low detection accuracy. Aiming at the above problems， an Ensemble learning and Autoencoder-based Outlier Detection （EAOD） algorithm was proposed. Firstly， the outlier values and outlier label values of the data objects were obtained by randomly changing the connection structure of the autoencoder generate different base detectors. Secondly， local region around the object was constructed according to the Euclidean distance between the data objects calculated by the nearest neighbor algorithm. Finally， based on the similarity between the outlier values and the outlier label values， the base detectors with strong detection ability in the region were selected and combined together， and the object outlier value after combination was used as the final outlier value judged by EAOD algorithm. In the experiments， compared with the AutoEncoder （AE） algorithm， the proposed algorithm has the Area Under receiver operating characteristic Curve （AUC） and Average Precision （AP） scores increased by 8.08 percentage points and 9.17 percentage points respectively on Cardio dataset； compared with the Feature Bagging （FB） ensemble learning algorithm， the proposed algorithm has the detection time cost reduced by 21.33% on Mnist dataset. Experimental results show that the proposed algorithm has good detection performance and real-time performance under unsupervised learning.

In feature learning process， the existing hashing methods cannot distinguish the importance of the feature information of each region， and cannot utilize the label information to explore the correlation between modalities. Therefore， an Adaptive Hybrid Attention Hashing for deep cross-modal retrieval （AHAH） model was proposed. Firstly， channel attention and spatial attention were combined by the weights obtained by autonomous learning to strengthen the attention to the relevant target area and weaken the attention to the irrelevant target area. Secondly， the similarity between modalities was expressed more finely through the statistical analysis of modality labels and quantification of similarity degrees to numbers between 0 and 1 by using the proposed similarity measurement method. Compared with the most advanced method Multi-Label Semantics Preserving Hashing （MLSPH） on four commonly used datasets MIRFLICKR-25K， NUS-WIDE， MSCOCO， and IAPR TC-12， when the hash code length is 16 bit， the proposed method has the retrieval mean Average Precision （mAP） increased by 2.25%， 1.75%， 6.8%， and 2.15%， respectively. In addition， ablation experiments and efficiency analysis also prove the effectiveness of the proposed method.