18 citations · 35 across the 12 of their papers we have counts for
12 papers
Efficient Adversarial Training via Criticality-Aware Fine-Tuning
Wenyun Li, Zheng Zhang, Dongmei Jiang +2
Vision Transformer (ViT) models have achieved remarkable performance across various vision tasks, with scalability being a key advantage when applied to large datasets. This scalab…
QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
Navid Azimi, Aditya Prakash, Yao Wang +1
Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications. To address this challenge, we…
Perception-Consistency Multimodal Large Language Models Reasoning via Caption-Regularized Policy Optimization
Songjun Tu, Qichao Zhang, Jingbo Sun +6
While multimodal large language models excel at tasks that integrate visual perception with symbolic reasoning, their performance is often undermined by a critical vulnerability: p…
DS-Det: Single-Query Paradigm and Attention Disentangled Learning for Flexible Object Detection
Guiping Cao, Xiangyuan Lan, Wenjian Huang +3
Popular transformer detectors have achieved promising performance through query-based learning using attention mechanisms. However, the roles of existing decoder query types (e.g.,…
Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection
Guiping Cao, Wenjian Huang, Xiangyuan Lan +3
Small Object Detection (SOD) poses significant challenges due to limited information and the model's low class prediction score. While Transformer-based detectors have shown promis…
SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition
Feng Lu, Tong Jin, Xiangyuan Lan +4
Recent studies show that the visual place recognition (VPR) method using pre-trained visual foundation models can achieve promising performance. In our previous work, we propose a…
Toward Visual Grounding: A Survey
Linhui Xiao, Xiaoshan Yang, Xiangyuan Lan +2
Visual Grounding, also known as Referring Expression Comprehension and Phrase Grounding, aims to ground the specific region(s) within the image(s) based on the given expression tex…
CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs
Zhehan Kan, Ce Zhang, Zihan Liao +7
Large Vision-Language Model (LVLM) systems have demonstrated impressive vision-language reasoning capabilities but suffer from pervasive and severe hallucination issues, posing sig…
EMMA: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment
Yifei Xing, Xiangyuan Lan, Ruiping Wang +4
Mamba-based architectures have shown to be a promising new direction for deep learning models owing to their competitive performance and sub-quadratic deployment speed. However, cu…
OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
Hao Wang, Pengzhen Ren, Zequn Jie +8
Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training. Existing methods…
Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
Feng Lu, Lijun Zhang, Xiangyuan Lan +3
Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perc…
CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition
Feng Lu, Xiangyuan Lan, Lijun Zhang +3
Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations. These networks typically produce a global represe…