DNA-Depth is currently one of the better-performing lightweight models for monocular depth estimation, but predicting detailed information remains challenging. This paper proposes a method called EUF-Depth, which can efficiently fuse encoder features based on DNA-Depth. Firstly, a feature fusion strategy is introduced to improve the utilization of important features through a learnable method. Then, the channel attention in the decoder part is replaced with Coordinate Attention (CA), incorporating positional information to enhance model prediction accuracy. Experiments on the KITTI benchmark demonstrate that EUF-Depth outperforms DNA-Depth in all metrics. The error evaluation metrics decrease by an average of 2% and the accuracy evaluation metrics increase by an average of 1%.
In order to effectively obtain word-level semantic knowledge and address the issue of inaccurate extraction of salient information during feature extraction, this paper proposes a named entity recognition method that combines the RoBERTa-WWM (A Robustly Optimized BERT Pre-training Approach-Whole Word Masking) pretrained model with attention mechanisms. Firstly, the RoBERTa-WWM model is trained to obtain word-level semantic knowledge representation. This semantic representation is then sequentially inputted into a bidirectional long short-term memory (BiLSTM) network, where attention mechanisms are applied to assign weights to key information. Finally, a conditional random field (CRF) is used to obtain the globally optimal labels. Experimental results demonstrate that compared to traditional named entity algorithms that use character-level semantic knowledge, the proposed method achieves a 1.25% improvement in the F1 score. Moreover, further improvement of 2.06% in F1 score is achieved by weighting the key information, indicating the strong performance of the proposed model in Chinese named entity recognition task.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.