Abstract: A method and apparatus with image quality assessment are provided. A method includes generating first modality data corresponding to an image pair of different levels of quality and related visual information, generating, based on the first modality data, a first relative quality value corresponding to predetermined quality indices, generating second modality data corresponding to a query that is based on the first relative quality value, retrieving, based on first and second modality features, at least one piece of second image quality assessment data from a dataset comprising pieces of first image quality assessment data, where the first and second modality features are respectively generated based on the first and second modality data, generating third image quality assessment data corresponding to the image pair by applying the first and second modality features and the at least one piece of second image quality assessment data to an augmented language model.

Abstract: A method includes: generating first text words by performing text recognition on a frame sequence component of an audio-video (AV) input; generating second text words by performing first speech-to-text (STT) conversion on an audio component of the AV input; determining a chronological sequence of groupings of the first text words and the second text words, each grouping including at least one of the first text words and at least one of the second text words, wherein the groupings are formed based on inter-frame similarities computed between frames of the frame sequence; generating keywords of the respective groupings; and generating output text words by performing second STT conversion on the audio component of AV input, wherein the second STT conversion is informed by the keywords.

Abstract: A method and device with personalized image generation are provided. The method includes generating a preference vector of a user based on a plurality of preset properties for personalized image generation, generating a personalized image for the user based on the preference vector and a connection relationship between an image generative model and a plurality of characterization layers corresponding to the plurality of properties, and outputting the personalized image in response to an image generation command.

Abstract: A processor-implemented method includes generating, from a video comprising speech data and a plurality of frame images, a first content set comprising a partial speech and a representative frame image, separating a digital document related to the video into a plurality of candidate content sets, mapping a second content set among the candidate content sets, which is related to the first content set, to the first content set, generating text corresponding to the representative frame image of the first content set, based on the first content set and the second content set, and generating an image-text pair comprising the representative frame image and the generated text.

Abstract: A processor-implemented method includes obtaining a plurality of prompts indicating image quality with different levels, and generating a plurality of visual media of the same content corresponding to the respective prompts by applying the obtained prompts to a visual medium generation model, wherein the visual medium generation model is trained based on a loss function related to a level of image quality evaluated for an output visual medium and a level of image quality indicated by an input prompt.

Abstract: A processor-implemented method with high-resolution (HR) image restoration includes mapping an input image with low resolution (LR) to a feature map of a latent domain, generating, using a diffusion model, an HR feature in which a certain frequency component corresponding to the feature map is restored, and based on the HR feature and coordinate information and pixel information of an HR image at a target magnification, restoring the HR image at the target magnification corresponding to the feature map.

Abstract: An apparatus for image analysis includes: an image acquisition unit for stacking a plurality of two-dimensional image data in a predetermined order; a three-dimensional image generation unit for generating a plurality of three-dimensional data on the basis of different types of multiple items of information for the plurality of two-dimensional image data in a stacked form from the image acquisition unit; and a deep learning algorithm analysis unit for applying a two-dimensional convolutional neural network to each of the plurality of three-dimensional data from the three-dimensional image generation unit, and combining results of applying the two-dimensional convolutional neural network to the plurality of three-dimensional data.

Abstract: An apparatus for image analysis includes: an image acquisition unit for stacking a plurality of two-dimensional image data in a predetermined order; a three-dimensional image generation unit for generating a plurality of three-dimensional data on the basis of different types of multiple items of information for the plurality of two-dimensional image data in a stacked form from the image acquisition unit; and a deep learning algorithm analysis unit for applying a two-dimensional convolutional neural network to each of the plurality of three-dimensional data from the three-dimensional image generation unit, and combining results of applying the two-dimensional convolutional neural network to the plurality of three-dimensional data.