10 citations · 19 across the 8 of their papers we have counts for
8 papers
GUIDE: Interpretable GUI Agent Evaluation via Hierarchical Diagnosis
Yuwen Zhai, Runze Li, Liang Wang +6
Evaluating GUI agents presents a distinct challenge: trajectories are long, visually grounded, and open-ended, yet evaluation must be both accurate and interpretable. Existing appr…
EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration
Runze Li, Yuwen Zhai, Bo Xu +5
Contemporary GUI agents, while increasingly capable due to advances in Large Vision-Language Models (VLMs), often operate with a critical limitation: they treat each task in isolat…
LOST: Low-rank and Sparse Pre-training for Large Language Models
Jiaxi Li, Lu Yin, Li Shen +6
While large language models (LLMs) have achieved remarkable performance across a wide range of tasks, their massive scale incurs prohibitive computational and memory costs for pre-…
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…