University Media Content Detection and Classification Based on Information Fusion Algorithm by Shuntao Zhang, Qinglan Yu, Tianming Yang, Kai Peng

“…This essay mainly introduces the technology of university media content detection and classification based on information fusion algorithm and focuses on the application of university multimedia content detection, analysis, and understanding, to explore the image discrimination auxiliary attribute feature learning and content association prediction and classification. …”
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Action-driven contrastive representation for reinforcement learning. by Minbeom Kim, Kyeongha Rho, Yong-Duk Kim, Kyomin Jung

“…In reinforcement learning, reward-driven feature learning directly from high-dimensional images faces two challenges: sample-efficiency for solving control tasks and generalization to unseen observations. …”
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An improved multi-scale branching convolutional neural network for rolling bearing fault diagnosis. by Meng Xu, Yaowei Shi, Minqiang Deng, Yang Liu, Xue Ding, Aidong Deng

“…The proposed method first applies the multiscale feature learning strategy to extract rich and compelling fault information from diverse and complex vibration signals. …”
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Scaling object detection by transferring learning by Liu, Yizheng

“…The detection network and WTN are trained by Objects 365 dataset which is the large-scale object detection dataset and works well in feature learning. The experimental results show that the performance of WTN is improved.…”
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Robust real-time visual tracking by Liu, Ting

“…In this thesis we present four different tracking algorithms which exploit the sparse coding, part-based model, color feature learning and convolutional network features to handle the aforementioned challenges.Extensive experiments have been done respectively to prove the effectiveness of our proposed trackers.…”
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Contrastive learning for unsupervised radar place recognition by Gadd, M, De Martini, D, Newman, P

“…Our method is based on invariant instance feature learning but is tailored for the task of re-localisation by exploiting for data augmentation the temporal successivity of data as collected by a mobile platform moving through the scene smoothly. …”

Efficient and Robust: A Cross-Modal Registration Deep Wavelet Learning Method for Remote Sensing Images by Dou Quan, Huiyuan Wei, Shuang Wang, Yi Li, Jocelyn Chanussot, Yanhe Guo, Biao Hou, Licheng Jiao

“…Deep convolutional networks are powerful for local feature learning and have shown advantages in image matching and registration. …”
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Improving object detection quality with structural constraints. by Zihao Rong, Shaofan Wang, Dehui Kong, Baocai Yin

“…Structural constraint supervises feature learning in classification and localization network branches with Fisher Loss and Equi-proportion Loss respectively, by requiring feature similarities of training sample pairs to be consistent with corresponding ground truth label similarities. …”
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An Asymmetric Contrastive Loss for Handling Imbalanced Datasets by Valentino Vito, Lim Yohanes Stefanus

“…The learning process is typically conducted using a two-stage training architecture, and it utilizes the contrastive loss (CL) for its feature learning. Contrastive learning has been shown to be quite successful in handling imbalanced datasets, in which some classes are overrepresented while some others are underrepresented. …”
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<inline-formula><tex-math notation="LaTeX">$C^{2}N^{2}$</tex-math></inline-formula>: Complex-Valued Contourlet Neural Network by Mengkun Liu, Licheng Jiao, Xu Liu, Lingling Li, Fang Liu, Shuyuan Yang, Yuwei Guo, Puhua Chen

“…This is the key to feature learning representation of high-order singularity. …”
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A Concise Introduction to Machine Learning / by Faul, A. C. (Anita C.), author

An Unsupervised Character Recognition Method for Tibetan Historical Document Images Based on Deep Learning by Xiaojuan Wang, Weilan Wang

“…Then, the character baseline information is introduced, and a fine-grained feature learning strategy is proposed. For the samples above and below the baseline, the Up-CNN recognition model and Down-CNN recognition model are established. …”
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ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion Estimation by Oliver W. Layton

“…This design affords fast, local feature learning across parallel modules in each network layer. …”
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Orientation-Encoding CNN for Point Cloud Classification and Segmentation by Hongbin Lin, Wu Zheng, Xiuping Peng

“…By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. …”
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Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples by Shirin Hassanzadeh, Habibollah Danyali, Mohammad Sadegh Helfroush

“…Then MKL is utilized to enhance the feature learning process and obtain an optimum combination of some specified kernels. …”
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Digital construction of higher education management based on multimodal machine database by Liu Shen

“…Then, the higher education management system is constructed based on semi-supervised fusion feature learning and homogeneous multimodal features of multimodal machines, and the system architecture and database design are explained in detail. …”
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Bearing Fault Diagnosis based on Feature Visualization and Depth Adaptive Network by Hong Jiang, Yu Feng, Rong Fu

“…It can not only select the similar modal components of the noisy signal and the original signal to improve the signal-to-noise ratio of the noisy signal，but also eliminate the similar modal components of different types of signals to highlight the signal characteristics； then，the signal is reconstructed by using the selected IMFs，and the reconstructed signal is visualized based on GAF transform； finally，the depth adaptive network is used for feature learning and state recognition. The results show that the accuracy of the proposed method is 94.97%，which is better than the common vibration signal fault diagnosis methods，and the proposed method can also suppress the noise and has good robustness，which provides a reasonable idea for the intelligent and accurate diagnosis of bearings.…”
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Spatiotemporal image fusion using multiscale attention-aware two-stream convolutional neural networks by Yuehong Chen, Yong Ge

“…With a coarse image at the prediction date and two pairs of coarse and fine images at other dates as inputs, it employs a multiscale module to characterize different sizes of objects and a spatial and channel attention module to emphasize important information in feature learning. Two experiments on real Landsat and MODIS images are conducted to demonstrate the effectiveness of the proposed MACNN and it outperforms four existing STF methods in both visual and quantitative.…”
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3D object recognition with a linear time‐varying system of overlay layers by Mohammad Sohrabi Nasrabadi, Reza Safabakhsh

“…The challenge is in selecting appropriate robust features with tolerable computing costs. Feature learning attempts to solve the feature extraction problem through a learning process using various samples of the objects. …”
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Learning a spatial-temporal texture transformer network for video inpainting by Pengsen Ma, Tao Xue

“…Such a design encourages joint feature learning across the input and reference frames. …”
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