17 citations · 60 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…
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…
Defensive Patches for Robust Recognition in the Physical World
Jiakai Wang, Zixin Yin, Pengfei Hu +5
To operate in real-world high-stakes environments, deep learning systems have to endure noises that have been continuously thwarting their robustness. Data-end defense, which impro…
Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection
Renshuai Tao, Yanlu Wei, Xiangjian Jiang +6
Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resul…
Learning from Multiple Annotators by Incorporating Instance Features
Jingzheng Li, Hailong Sun, Jiyi Li +3
Learning from multiple annotators aims to induce a high-quality classifier from training instances, where each of them is associated with a set of possibly noisy labels provided by…
Multi-Pretext Attention Network for Few-shot Learning with Self-supervision
Hainan Li, Renshuai Tao, Jun Li +4
Few-shot learning is an interesting and challenging study, which enables machines to learn from few samples like humans. Existing studies rarely exploit auxiliary information from…
Diversifying Sample Generation for Accurate Data-Free Quantization
Xiangguo Zhang, Haotong Qin, Yifu Ding +6
Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical…
Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images
Renshuai Tao, Yanlu Wei, Hainan Li +4
Security inspection is X-ray scanning for personal belongings in suitcases, which is significantly important for the public security but highly time-consuming for human inspectors.…
Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module
Yanlu Wei, Renshuai Tao, Zhangjie Wu +3
Security inspection often deals with a piece of baggage or suitcase where objects are heavily overlapped with each other, resulting in an unsatisfactory performance for prohibited…