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Found 7 papers, 2 papers with code
Judge Anything: MLLM as a Judge Across Any Modality
Furthermore, JudgeAnything evaluates the judging capabilities of 5 advanced (e. g., GPT-4o and Gemini-2. 0-Flash) from the perspectives of Pair Comparison and Score Evaluation, providing a standardized testbed that incorporates human judgments and detailed rubrics.
Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method
To address this, we proposed the DepL-GCN, i. e., Depression Level classification based on GCN model.
FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention
Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fraud.
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
(2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests.
Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study
To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements.
Deep Learning-based Assessment of Hepatic Steatosis on chest CT
A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model.
Deep learning-based detection of intravenous contrast in computed tomography scans
The fine-tuned model on chest CTs yielded an AUC: 1. 0 for the internal validation set (n = 53), and AUC: 0. 980 for the external validation set (n = 402).
