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Found 27 papers, 14 papers with code
Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric
To address this challenge, we conduct a pioneering study on human activity AGV quality assessment, focusing on visual quality evaluation and the identification of semantic distortions.
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis
Artificial Intelligence (AI) has demonstrated significant capabilities in various fields, and in areas such as human-computer interaction (HCI), embodied intelligence, and the design and animation of virtual digital humans, both practitioners and users are increasingly concerned with AI's ability to understand and express emotion.
Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming
For the subjective QoE study, we introduce the first live video streaming QoE dataset, TaoLive QoE, which consists of $42$ source videos collected from real live broadcasts and $1, 155$ corresponding distorted ones degraded due to a variety of streaming distortions, including conventional streaming distortions such as compression, stalling, as well as live streaming-specific distortions like frame skipping, variable frame rate, etc.
3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents
To provide a valuable resource for future research and development in 3D content generation and quality assessment, the dataset has been open-sourced in https://github. com/zyj-2000/3DGCQA.
Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency
To address this problem, we design a multi-branch deep neural network (DNN) to assess the quality of UHD images from three perspectives: global aesthetic characteristics, local technical distortions, and salient content perception.
SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression
Just noticeable distortion (JND), representing the threshold of distortion in an image that is minimally perceptible to the human visual system (HVS), is crucial for image compression algorithms to achieve a trade-off between transmission bit rate and image quality.
Benchmarking AIGC Video Quality Assessment: A Dataset and Unified Model
For the objective perspective, we establish a benchmark for evaluating existing quality assessment metrics on the LGVQ dataset, which reveals that current metrics perform poorly on the LGVQ dataset.
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models
Traditional image steganography focuses on concealing one image within another, aiming to avoid steganalysis by unauthorized entities.
GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
Assessing action quality is both imperative and challenging due to its significant impact on the quality of AI-generated videos, further complicated by the inherently ambiguous nature of actions within AI-generated video (AIGV).
Enhancing Blind Video Quality Assessment with Rich Quality-aware Features
Motivated by previous researches that leverage pre-trained features extracted from various computer vision models as the feature representation for BVQA, we further explore rich quality-aware features from pre-trained blind image quality assessment (BIQA) and BVQA models as auxiliary features to help the BVQA model to handle complex distortions and diverse content of social media videos.
Dual-Branch Network for Portrait Image Quality Assessment
We employ the fidelity loss to train the model via a learning-to-rank manner to mitigate inconsistencies in quality scores in the portrait image quality assessment dataset PIQ.
Large Multi-modality Model Assisted AI-Generated Image Quality Assessment
Traditional deep neural network (DNN)-based image quality assessment (IQA) models leverage convolutional neural networks (CNN) or Transformer to learn the quality-aware feature representation, achieving commendable performance on natural scene images.
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for Text-Guided QR Code Generation
This approach harnesses the potent generation capabilities of stable-diffusion models, navigating the trade-off between image aesthetics and QR code scannability.
Perceptual Quality Assessment for Video Frame Interpolation
To evaluate the quality of VFI frames without reference videos, a no-reference perceptual quality assessment method is proposed in this paper.
Exploring the Naturalness of AI-Generated Images
In this paper, we take the first step to benchmark and assess the visual naturalness of AI-generated images.
SingingHead: A Large-scale 4D Dataset for Singing Head Animation
Singing, as a common facial movement second only to talking, can be regarded as a universal language across ethnicities and cultures, plays an important role in emotional communication, art, and entertainment.
BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment
The quality score of a banding image is generated by pooling the banding detection maps masked by the spatial frequency filters.
StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability
Indeed, most existing quality assessment models evaluate video quality as a whole without specifically taking the subjective experience of video stability into consideration.
RAWIW: RAW Image Watermarking Robust to ISP Pipeline
To achieve end-to-end training of the framework, we integrate a neural network that simulates the ISP pipeline to handle the RAW-to-RGB conversion process.
Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images
Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc.
Learning Invisible Markers for Hidden Codes in Offline-to-Online Photography
In addition, the automatic localization of hidden codes significantly reduces the time of manually correcting geometric distortions for photos, which is a revolutionary innovation for information hiding in mobile applications.
Deep Natural Language Processing for LinkedIn Search
Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Deep Natural Language Processing for LinkedIn Search Systems
Many search systems work with large amounts of natural language data, e. g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Robust Invisible Hyperlinks in Physical Photographs Based on 3D Rendering Attacks
To train a robust decoder against the physical distortion from the real world, a distortion network based on 3D rendering is inserted between the encoder and the decoder to simulate the camera imaging process.
MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation
MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations.
Mathematical Software Computational Engineering, Finance, and Science Numerical Analysis
