33 citations · 79 across the 20 of their papers we have counts for
21 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…
Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression
Jie Liu, Tiexin Qin, Hui Liu +5
In this work, we address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment. While Test-time Training (TTT) approaches show promise for this t…
Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need
Kecheng Chen, Pingping Zhang, Hui Liu +6
We have recently witnessed that ``Intelligence" and `` Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a ge…
Test-time adaptation for image compression with distribution regularization
Kecheng Chen, Pingping Zhang, Tiexin Qin +3
Current test- or compression-time adaptation image compression (TTA-IC) approaches, which leverage both latent and decoder refinements as a two-step adaptation scheme, have potenti…
Image Provenance Analysis via Graph Encoding with Vision Transformer
Keyang Zhang, Chenqi Kong, Shiqi Wang +2
Recent advances in AI-powered image editing tools have significantly lowered the barrier to image modification, raising pressing security concerns those related to spreading misinf…
Exposing AI-generated Videos: A Benchmark Dataset and a Local-and-Global Temporal Defect Based Detection Method
Peisong He, Leyao Zhu, Jiaxing Li +2
The generative model has made significant advancements in the creation of realistic videos, which causes security issues. However, this emerging risk has not been adequately addres…
Pixel-Inconsistency Modeling for Image Manipulation Localization
Chenqi Kong, Anwei Luo, Shiqi Wang +3
Digital image forensics plays a crucial role in image authentication and manipulation localization. Despite the progress powered by deep neural networks, existing forgery localizat…
The Age of Synthetic Realities: Challenges and Opportunities
João Phillipe Cardenuto, Jing Yang, Rafael Padilha +7
Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of…
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…
Geometric Prior Based Deep Human Point Cloud Geometry Compression
Xinju Wu, Pingping Zhang, Meng Wang +3
The emergence of digital avatars has raised an exponential increase in the demand for human point clouds with realistic and intricate details. The compression of such data becomes…
Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation
Chenqi Kong, Haoliang Li, Shiqi Wang
Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing…
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
Chenqi Kong, Kexin Zheng, Yibing Liu +3
Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and pri…
Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection
Jiaxing Li, Chenqi Kong, Shiqi Wang +1
The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to th…
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…
Digital and Physical Face Attacks: Reviewing and One Step Further
Chenqi Kong, Shiqi Wang, Haoliang Li
With the rapid progress over the past five years, face authentication has become the most pervasive biometric recognition method. Thanks to the high-accuracy recognition performanc…
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Tiexin Qin, Shiqi Wang, Haoliang Li
Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark…
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Yibing Liu, Haoliang Li, Yangyang Guo +3
Attention mechanisms are dominating the explainability of deep models. They produce probability distributions over the input, which are widely deemed as feature-importance indicato…
Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales
Chenqi Kong, Baoliang Chen, Haoliang Li +3
The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society. Generally…
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…