1 citations · 2 across the 12 of their papers we have counts for
12 papers
G-MemLLM: Gated Latent Memory Augmentation for Long-Context Reasoning in Large Language Models
Xun Xu
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, yet they remain constrained by the finite capacity of their context window…
The Reward Model Selection Crisis in Personalized Alignment
Fady Rezk, Yuangang Pan, Chuan-Sheng Foo +4
Personalized alignment from preference data has focused primarily on improving personal reward model (RM) accuracy, with the implicit assumption that better preference ranking tran…
Enhancing Generalization of Depth Estimation Foundation Model via Weakly-Supervised Adaptation with Regularization
Yan Huang, Yongyi Su, Xin Lin +2
The emergence of foundation models has substantially advanced zero-shot generalization in monocular depth estimation (MDE), as exemplified by the Depth Anything series. However, gi…
Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs
Yongyi Su, Haojie Zhang, Shijie Li +11
Multimodal large language models (MLLMs) have advanced rapidly in recent years. However, existing approaches for vision tasks often rely on indirect representations, such as genera…
Exploring and Reshaping the Weight Distribution in LLM
Chunming Ye, Songzhou Li, Xu Xu
The performance of Large Language Models is influenced by their characteristics such as architecture, model sizes, decoding methods and so on. Due to differences in structure or fu…
AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
Jingyi Liao, Yongyi Su, Rong-Cheng Tu +6
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities across diverse domains, their application to specialized anomaly detection (AD) remains constrain…
Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift
Jingyi Liao, Xun Xu, Yongyi Su +4
Anomaly detection plays a crucial role in quality control for industrial applications. However, ensuring robustness under unseen domain shifts such as lighting variations or sensor…
Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model
Yushu Li, Yongyi Su, Adam Goodge +2
Vision-language models (VLMs) have revolutionized machine learning by leveraging large pre-trained models to tackle various downstream tasks. Although label, training, and data eff…
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
Yongyi Su, Yushu Li, Nanqing Liu +4
Test-time adaptation (TTA) updates the model weights during the inference stage using testing data to enhance generalization. However, this practice exposes TTA to adversarial risk…
Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale
Wenzhen Zheng, Wenbo Pan, Xu Xu +3
In recent years, Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence. However, training these models from scratch requires substantia…
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection
Yushu Li, Yongyi Su, Xulei Yang +2
Existing test-time adaptation (TTA) approaches often adapt models with the unlabeled testing data stream. A recent attempt relaxed the assumption by introducing limited human annot…
Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
Haojie Zhang, Yongyi Su, Xun Xu +1
The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prom…