4 citations · 4 across the 8 of their papers we have counts for
10 papers
Context-dependent agent evaluation with orthogonal equilibrium learning
Haorui Ma, Zehua Zang, Jiangmeng Li +3
Many applications require to evaluate agents under contextual information (e.g., a prompt, task, or user group). We study how to perform such context-dependent agent evaluation fro…
D3ER: Supporting Multi-Modal Recommendation via Disentangle and Distillation-based Dynamic Ensemble
Bingnan Wang, Yi Li, Xiongxin Tang +2
Incorporating items' information shared among multiple modalities into a fused representation, multi-modal recommendation (MR) has demonstrated documented success than canonical un…
Multi-modal Test-time Adaptation via Adaptive Probabilistic Gaussian Calibration
Jinglin Xu, Yi Li, Chuxiong Sun +3
Multi-modal test-time adaptation (TTA) enhances the resilience of benchmark multi-modal models against distribution shifts by leveraging the unlabeled target data during inference.…
AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning
Fei Song, Yi Li, Jiangmeng Li +4
Multi-prompt learning methods have emerged as an effective approach for facilitating the rapid adaptation of vision-language models to downstream tasks with limited resources. Exis…
Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models
Fei Song, Yi Li, Rui Wang +3
Test-time prompt tuning for vision-language models has demonstrated impressive generalization capabilities under zero-shot settings. However, tuning the learnable prompts solely ba…
BayesTTA: Continual-Temporal Test-Time Adaptation for Vision-Language Models via Gaussian Discriminant Analysis
Shuang Cui, Jinglin Xu, Yi Li +6
Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but degrade significantly under \textit{temporally evolving distribution shifts} common in real-worl…
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Shuang Cui, Yi Li, Jiangmeng Li +4
Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation…
Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
Jiangmeng Li, Bin Qin, Qirui Ji +4
Leveraging the development of structural causal model (SCM), researchers can establish graphical models for exploring the causal mechanisms behind machine learning techniques. As t…
Revisiting Spurious Correlation in Domain Generalization
Bin Qin, Jiangmeng Li, Yi Li +4
Without loss of generality, existing machine learning techniques may learn spurious correlation dependent on the domain, which exacerbates the generalization of models in out-of-di…
Interventional Imbalanced Multi-Modal Representation Learning via -Generalization Front-Door Criterion
Yi Li, Fei Song, Changwen Zheng +3
Multi-modal methods establish comprehensive superiority over uni-modal methods. However, the imbalanced contributions of different modalities to task-dependent predictions constant…