activity
20202026
most citedX-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

17 citations · 38 across the 11 of their papers we have counts for

collaborators

13 papers

cs.CV2026

Co-Evolutionary Prompt Optimization with Cross-Category Transfer for Zero-Shot Anomaly Detection

Sisi Zhu, Changwei Yu, Renshuai Tao +1

Zero-shot anomaly detection (ZSAD) has gained significant attention for its practical value in industrial inspection. Recently, CLIP-based approaches have been widely adopted in ZS…

cs.SE2025★ 1 cited

From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence

Jian Yang, Xianglong Liu, Weifeng Lv +68

Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…

cs.CV2025

Can a Second-View Image Be a Language? Geometric and Semantic Cross-Modal Reasoning for X-ray Prohibited Item Detection

Chuang Peng, Renshuai Tao, Zhongwei Ren +2

Automatic X-ray prohibited items detection is vital for security inspection and has been widely studied. Traditional methods rely on visual modality, often struggling with complex…

cs.CV2024

MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models

Weilun Feng, Haotong Qin, Chuanguang Yang +7

Diffusion models have received wide attention in generation tasks. However, the expensive computation cost prevents the application of diffusion models in resource-constrained scen…

cs.CL2024

Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation

Yanni Xue, Haojie Hao, Jiakai Wang +5

While neural machine translation (NMT) models achieve success in our daily lives, they show vulnerability to adversarial attacks. Despite being harmful, these attacks also offer be…

cs.CR2023★ 17 cited

X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

Aishan Liu, Jun Guo, Jiakai Wang +6

Adversarial attacks are valuable for evaluating the robustness of deep learning models. Existing attacks are primarily conducted on the visible light spectrum (e.g., pixel-wise tex…