Instruction tuning has significantly advanced large language models (LLMs) such as
ChatGPT, enabling them to align with human instructions across diverse tasks. However …

Multimodal large language models (MLLMs) enhance the capabilities of standard large
language models by integrating and processing data from multiple modalities, including text …

Perceptual quality assessment of the videos acquired in the wilds is of vital importance for
quality assurance of video services. The inaccessibility of reference videos with pristine …

Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image
quality in a deterministic way by explicitly comparing the features, gauging how severely …

The latest advances in full-reference image quality assessment (IQA) involve unifying
structure and texture similarity based on deep representations. The resulting Deep Image …

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved
notable success, which is capable of achieving superior performance across various tasks …

No-reference bitstream-layer models for point cloud quality assessment (PCQA) use the
information extracted from a bitstream for real-time and nonintrusive quality monitoring. We …

In this paper, we present a simple yet effective continual learning method for blind image
quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability trade …

The fundamental conflict between the enormous space of adaptive streaming videos and the
limited capacity for subjective experiment casts significant challenges to objective Quality-of …

Objective Image Quality Assessment (IQA) aims to design computational models that can
automatically predict the perceived quality of images. The state-of-the-art full-reference IQA …