15 citations · 52 across the 24 of their papers we have counts for
11 papers · 1 filter
Scalpel: Fine-Grained Alignment of Attention Activation Manifolds via Mixture Gaussian Bridges to Mitigate Multimodal Hallucination
Ziqiang Shi, Rujie Liu, Shanshan Yu +2
Rapid progress in large vision-language models (LVLMs) has achieved unprecedented performance in vision-language tasks. However, due to the strong prior of large language models (L…
SchröMind: Mitigating Hallucinations in Multimodal Large Language Models via Solving the Schrödinger Bridge Problem
Ziqiang Shi, Rujie Liu, Shanshan Yu +2
Recent advancements in Multimodal Large Language Models (MLLMs) have achieved significant success across various domains. However, their use in high-stakes fields like healthcare r…
Attention to details, logits to truth: visual-aware attention and logits enhancement to mitigate hallucinations in LVLMs
Jingyi Wang, Fei Li, Rujie Liu
Existing Large Vision-Language Models (LVLMs) exhibit insufficient visual attention, leading to hallucinations. To alleviate this problem, some previous studies adjust and amplify…
Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025
Jingzhe Ma, Meng Zhang, Jianlong Yu +34
Human identification at a distance (HID) is challenging because traditional biometric modalities such as face and fingerprints are often difficult to acquire in real-world scenario…
RTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking
Song Guo, Rujie Liu, Narishige Abe
Data association is an essential part in the tracking-by-detection based Multi-Object Tracking (MOT). Most trackers focus on how to design a better data association strategy to imp…
HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning
Ziqiang Shi, Liu Liu, Zhongling Liu +3
The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for…
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks
Wei Shen, Fei Li, Rujie Liu
Training convolutional neural networks for image classification tasks usually causes information loss. Although most of the time the information lost is redundant with respect to t…
Learning to generate filters for convolutional neural networks
Wei Shen, Rujie Liu
Conventionally, convolutional neural networks (CNNs) process different images with the same set of filters. However, the variations in images pose a challenge to this fashion. In t…
Tackling Early Sparse Gradients in Softmax Activation Using Leaky Squared Euclidean Distance
Wei Shen, Rujie Liu
Softmax activation is commonly used to output the probability distribution over categories based on certain distance metric. In scenarios like one-shot learning, the distance metri…
Generating Attention from Classifier Activations for Fine-grained Recognition
Wei Shen, Rujie Liu
Recent advances in fine-grained recognition utilize attention maps to localize objects of interest. Although there are many ways to generate attention maps, most of them rely on so…
Learning Residual Images for Face Attribute Manipulation
Wei Shen, Rujie Liu
Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challengin…