10 citations · 26 across the 6 of their papers we have counts for
6 papers
AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
Yipo Huang, Quan Yuan, Xiangfei Sheng +6
With collective endeavors, multimodal large language models (MLLMs) are undergoing a flourishing development. However, their performances on image aesthetics perception remain inde…
u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
Jinjin Xu, Liwu Xu, Yuzhe Yang +5
Recent advancements in multi-modal large language models (MLLMs) have led to substantial improvements in visual understanding, primarily driven by sophisticated modality alignment…
CLIP Brings Better Features to Visual Aesthetics Learners
Liwu Xu, Jinjin Xu, Yuzhe Yang +3
Image Aesthetics Assessment (IAA) is a challenging task due to its subjective nature and expensive manual annotations. Recent large-scale vision-language models, such as Contrastiv…
Mixed Sample Augmentation for Online Distillation
Yiqing Shen, Liwu Xu, Yuzhe Yang +2
Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks. Previous empirical analysis has il…
Personalized Image Aesthetics Assessment with Rich Attributes
Yuzhe Yang, Liwu Xu, Leida Li +4
Personalized image aesthetics assessment (PIAA) is challenging due to its highly subjective nature. People's aesthetic tastes depend on diversified factors, including image charact…
Self-Distillation from the Last Mini-Batch for Consistency Regularization
Yiqing Shen, Liwu Xu, Yuzhe Yang +2
Knowledge distillation (KD) shows a bright promise as a powerful regularization strategy to boost generalization ability by leveraging learned sample-level soft targets. Yet, emplo…