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Found 7 papers, 6 papers with code
WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification
Specifically, the accuracy of mixed-echoic plaques classification increased by approximately 3. 3%, demonstrating the effectiveness of our approach.
GroundingGPT:Language Enhanced Multi-modal Grounding Model
Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input.
A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification
The segmentation and classification of carotid plaques in ultrasound images play important roles in the treatment of atherosclerosis and assessment for the risk of stroke.
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data.
ConNER: Consistency Training for Cross-lingual Named Entity Recognition
We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropoutbased consistency training on labeled source-language data.
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation
A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with a deep understanding of the domain knowledge.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Data augmentation is an effective solution to data scarcity in low-resource scenarios.
