A memory module is adopted to reduce the reconstruction error, which is capable of enhancing the robustness of the autoencoder as a prototype memory module. The prediction of high-quality future frames can effectively prevent the reconstruction of abnormal frames, and the two branches can be ...
To address these issues, this paper proposes a novel video anomaly event detection algorithm based on a dual-channel autoencoder with key region feature enhancement. The goal is to preserve valuable information in the global context while focusing on regions with a high anomaly occurrence. Firstly,...
Figure 1. VideoMAE with dual masking. To improve the overall efficiency of computation and memory in video masked autoen- coding, we propose to mask the decoder as well and devise the dual masking strategy. Like encoder, we also apply a masking map to the deoco...
We propose an effective model with FRagment-based dual-channEL pretraining (FREL). Equipped with molecular fragments, FREL comprehensively employs masked autoencoder and contrastive learning to learn intra- and inter-molecule agreement, respectively. We further conduct extensive experiments on ten public...
ERGO2.037,54from the webserver (https://tcr2.cs.biu.ac.il/home) selecting the versions that did not include the McPAS dataset60in the training set. Both the Long Short-Term Memory (LSTM) and the AutoEncoders (AE) based were considered. ...
Our overall model is an integrated design in which an autoregressive model (AR) combines with an autoencoder (AE) structure. Scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Constructed by us, the Dual TCN-Attention Network (...
Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. In NeurIPS, 2022. 2 [66] Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. A closer look at spatiotemporal convolutions for action recognition. In ...
Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder. Appl. Energy 274, 115124 (2020). Article MATH Google Scholar Martinek, J. & Wagner, M. J. Efficient prediction of concentrating solar power plant productivity using data ...
Luo et al.18 proposed an improved autoencoder stacking method based on convolutional shortcuts and domain fusion strategies, replacing the sparse term Kullback–Leibler (KL) divergence in the original SAE with convolutional shortcuts, and introducing a domain fusion strategy to share feature ...
For data losses, autoencoders were applied to fill missing values, maintaining the essential features of the dataset. This technique draws on the autoencoder’s ability to capture and reconstruct intricate patterns, ensuring the integrity of imputed data [36]. Due to the intermittent tapping ...