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Found 5 papers, 3 papers with code
PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images
This oversight highlights a critical gap in the current research landscape, underscoring the need for dedicated databases catering to image-to-image scenarios, as well as more comprehensive databases that encompass a broader range of AI-generated image scenarios.
TIER: Text-Image Encoder-based Regression for AIGC Image Quality Assessment
However, most existing AIGCIQA methods regress predicted scores directly from individual generated images, overlooking the information contained in the text prompts of these images.
PSCR: Patches Sampling-based Contrastive Regression for AIGC Image Quality Assessment
To demonstrate the effectiveness of our proposed PSCR framework, we conduct extensive experiments on three mainstream AIGCIQA databases including AGIQA-1K, AGIQA-3K and AIGCIQA2023.
PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images
Although previous work has established several human perception-based AIGC image quality assessment (AIGCIQA) databases for text-generated images, the AI image generation technology includes scenarios like text-to-image and image-to-image, and assessing only the images generated by text-to-image models is insufficient.
Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model
There has been an encouraging progress in the affective states recognition models based on the single-modality signals as electroencephalogram (EEG) signals or peripheral physiological signals in recent years.
