4 citations · 5 across the 9 of their papers we have counts for
10 papers
LAST: The Last Query Token Guides Visual Token Pruning for Edge-Cloud Collaborative MLLM Inference
Feng Yang, Xinrui Ju, Keyang Zhang +6
Multimodal foundation models are reshaping edge-cloud visual intelligence from task-specific feature pipelines into token-based interfaces, where edge devices encode visual inputs…
LUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression
Chris Xing Tian, Chengkai Wu, Ziyu Wang +6
Large language model (LLM)-based lossless image compression methods typically represent pixel data through the native text interface of a pretrained model, converting pixel values…
Domain-Specific Data Generation Framework for RAG Adaptation
Chris Xing Tian, Weihao Xie, Zhen Chen +5
Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external retrieval to enable domain-grounded respo…
SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
Xinyu Luo, Kecheng Chen, Pao-Sheng Vincent Sun +3
Spiking Neural Networks (SNNs), as a biologically plausible alternative to Artificial Neural Networks (ANNs), have demonstrated advantages in terms of energy efficiency, temporal p…
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning
Hui Liu, Wenya Wang, Hao Sun +4
Large Language Models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities from few-shot demonstration exemplars. While recent learning-based demonstration se…
Gradient-Congruity Guided Federated Sparse Training
Chris Xing Tian, Yibing Liu, Haoliang Li +2
Edge computing allows artificial intelligence and machine learning models to be deployed on edge devices, where they can learn from local data and collaborate to form a global mode…
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
Yibing Liu, Chris Xing Tian, Haoliang Li +2
The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i.e., in-distribution (I…
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning
Yibing Liu, Chris Xing Tian, Haoliang Li +1
Learning invariant representations via contrastive learning has seen state-of-the-art performance in domain generalization (DG). Despite such success, in this paper, we find that i…
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
Chris Xing Tian, Haoliang Li, Yufei Wang +1
Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. However, due to the issue of limited dataset availability and the strict legal…
Neuron Coverage-Guided Domain Generalization
Chris Xing Tian, Haoliang Li, Xiaofei Xie +2
This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our m…