Search Results for author:
Found 8 papers, 1 papers with code
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space
To address the aforementioned problems, we propose a trustworthy multimodal sentiment ordinal network (TMSON) to improve performance in sentiment analysis.
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering.
X2-Softmax: Margin Adaptive Loss Function for Face Recognition
Learning the discriminative features of different faces is an important task in face recognition.
DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model
To alleviate the problem, we propose a spatial-temporal consistent diffusion framework DrivingDiffusion, to generate realistic multi-view videos controlled by 3D layout.
USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation
Rather than risk potential pseudo-labeling errors or learning confusion by forcefully classifying these regions, we consider them as uncertainty regions, exempting them from pseudo-labeling and allowing the network to self-learn.
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning
Our ICCE promotes intra-class compactness with inter-class separability on both seen and unseen classes in the embedding space and visual feature space.
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL).
Most Probable Evolution Trajectories in a Genetic Regulatory System Excited by Stable Lévy Noise
We especially examine those most probable trajectories from low concentration state to high concentration state (i. e., the likely transcription regime) for certain parameters, in order to gain insights into the transcription processes and the tipping time for the transcription likely to occur.
